Preparation

Clean the environment.

Set locations, and the working directory.

A package-installation function.

Load those packages.

We will create a datestamp and define the Utrecht Science Park Colour Scheme.

# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    tvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    R = summary(fit)$r.squared
    R.adj = summary(fit)$adj.r.squared
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
    
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("T-value...................:", round(tvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("R^2.......................:", round(R, 6), "\n")
    cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,9))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data...\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    zvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    dev <- fit$deviance
    nullDev <- fit$null.deviance
    modelN <- length(fit$fitted.values)
    R.l <- 1 - dev / nullDev
    R.cs <- 1 - exp(-(nullDev - dev) / modelN)
    R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("Z-value...................:", round(zvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
    cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
    cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

Background

Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b). While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.

Objectives

Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.

Methods

Blood

OLINK-platform

  • IL6: Interleukin 6. Entrez Gene: 3569. OLINK, plasma
  • MCP1: Monocyte chemotactic protein 1, MCP-1 (Chemokine (C-C motif) ligand 2, CCL2). Entrez Gene: 6347. OLINK, plasma

THESE DATA ARE NOT AVAILABLE YET

Plaque

Luminex-platform, measured by Luminex

  • MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/ug]. Measured in two experiments, variables MCP1 and MCP1_pg_ug_2015. We consider the latter the best possible measurement as this was corrected for plaque total protein concentration.
  • IL6: Interleuking 6 (IL6; Entrez Gene: 3569) concentration in plaque [pg/ug].
  • IL6R: Interleuking 6 receptor (IL6R; Entrez Gene: 3570) concentration in plaque [pg/ug].

FACS platform

  • IL6: Interleukin 6. Entrez Gene: 3569. Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL]

Loading data

Clinical data

Loading Athero-Express clinical data.

require(haven)

# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))

head(AEDB)
NA
NA
NA

Examine AEDB

We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class (type) it has, and what the variable description is.

There is an excellent post on this: https://www.r-bloggers.com/working-with-spss-labels-in-r/.

AEDB %>% sjPlot::view_df(show.type = TRUE,
                         show.frq = TRUE,
                         show.prc = TRUE,
                         show.na = TRUE, 
                         max.len = TRUE, 
                         wrap.labels = 20,
                         verbose = FALSE, 
                         use.viewer = FALSE,
                         file = paste0(OUT_loc, "/", Today, ".AEDB.dictionary.html")) 

Fixing and creating variables

We need to be very strict in defining symptoms. Therefore we will fix a new variable that groups symptoms at inclusion.

Coding of symptoms is as follows:

  • missing -999
  • Asymptomatic 0
  • TIA 1
  • minor stroke 2
  • Major stroke 3
  • Amaurosis fugax 4
  • Four vessel disease 5
  • Vertebrobasilary TIA 7
  • Retinal infarction 8
  • Symptomatic, but aspecific symtoms 9
  • Contralateral symptomatic occlusion 10
  • retinal infarction 11
  • armclaudication due to occlusion subclavian artery, CEA needed for bypass 12
  • retinal infarction + TIAs 13
  • Ocular ischemic syndrome 14
  • ischemisch glaucoom 15
  • subclavian steal syndrome 16
  • TGA 17

We will group as follows in Symptoms.5G:

  1. Asymptomatic > 0
  2. TIA > 1, 7, 13
  3. Stroke > 2, 3
  4. Ocular > 4, 14, 15
  5. Retinal infarction > 8, 11
  6. Other > 5, 9, 10, 12, 16, 17

We will also group as follows in AsymptSympt:

  1. Asymptomatic > 0
  2. TIA > 1, 7, 13 + Stroke > 2, 3
  3. Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17

We will also group as follows in AsymptSympt2G:

  1. Asymptomatic > 0
  2. TIA > 1, 7, 13 + Stroke > 2, 3 Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17
# Fix symptoms

attach(AEDB)

AEDB$sympt[is.na(AEDB$sympt)] <- -999

# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"

# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"

# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"

detach(AEDB)

# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
# 
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")
              
               Asymptomatic Ocular Other Retinal infarction Stroke  TIA <NA>
  Asymptomatic          333      0     0                  0      0    0    0
  Symptomatic             0    416   119                 43    732 1045    0
  <NA>                    0      0     0                  0      0    0 1103
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
# 
# rm(AEDB.temp)

We will also fix the plaquephenotypes variable.

Coding of symptoms is as follows:

  • missing -999
  • not relevant -888
  • fibrous 1
  • fibroatheromatous 2
  • atheromatous 3

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

table(AEDB$OverallPlaquePhenotype)

     atheromatous fibroatheromatous           fibrous 
              550               841              1439 
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the diabetes status variable. We define diabetes as history of a diagnosis and/or use of glucose-lowering medications.

# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

table(AEDB$DM.composite)

   0    1 
2764  985 
table(AEDB$DiabetesStatus)

Control (no Diabetes Dx/Med)                     Diabetes 
                        2764                          985 
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the smoking status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire.

  • diet801: are you a smoker?
  • diet802: did you smoke in the past?

We already have some variables indicating smoking status:

  • SmokingReported: patient has reported to smoke.
  • SmokingYearOR: smoking in the year of surgery?
  • SmokerCurrent: currently smoking?
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)

# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
# 
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))

cat("\nFixing smoking status.\n")

Fixing smoking status.
attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)

cat("\n* Current smoking status.\n")

* Current smoking status.
table(AEDB$SmokerCurrent,
      useNA = "ifany", 
      dnn = c("Current smoker"))
Current smoker
no data available/missing                        no                       yes                      <NA> 
                        0                      2364                      1308                       119 
cat("\n* Updated smoking status.\n")

* Updated smoking status.
table(AEDB$SmokerStatus,
      useNA = "ifany", 
      dnn = c("Updated smoking status"))
Updated smoking status
Current smoker      Ex-smoker   Never smoked           <NA> 
          1308           1814            389            280 
cat("\n* Comparing to 'SmokerCurrent'.\n")

* Comparing to 'SmokerCurrent'.
table(AEDB$SmokerStatus, AEDB$SmokerCurrent, 
      useNA = "ifany", 
      dnn = c("Updated smoking status", "Current smoker"))
                      Current smoker
Updated smoking status no data available/missing   no  yes <NA>
        Current smoker                         0    0 1308    0
        Ex-smoker                              0 1814    0    0
        Never smoked                           0  389    0    0
        <NA>                                   0  161    0  119
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the alcohol status variable.


# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)

table(AEDB$AlcoholUse)

  No  Yes 
1238 2345 
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on CAD_history, Stroke_history, and Peripheral.interv


# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)

table(AEDB$CAD_history)

   0    1 
2430 1285 
table(AEDB$Stroke_history)

   0    1 
2763  947 
table(AEDB$Peripheral.interv)

   0    1 
2579 1099 
table(AEDB$MedHx_CVD)

  No  yes 
1309 2475 
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

Athero-Express Biobank Study

Baseline characteristics

We are interested in the following variables at baseline.

  • Age (years)
  • Female sex (N, %)
  • Hypertension (N, %)
  • SBP (mmHg)
  • DBP (mmHg)
  • Diabetes mellitus (N, %)
  • Total cholesterol levels (mg/dL)
  • LDL cholesterol levels (mg/dL)
  • HDL cholesterol levels (mg/dL)
  • Triglyceride levels (mg/dL)
  • Use of statins (N, %)
  • Use of antiplatelet drugs (N, %)
  • BMI (kg/m²)
  • Smoking status (N, %)
    • Never smokers
    • Ex-smokers
    • Current smokers
  • History of CAD (N, %)
  • History of PAD (N, %)
  • Clinical manifestations
    • Asymptomatic
    • Amaurosis fugax
    • TIA
    • Stroke
  • eGFR (mL/min/1.73 m²)
  • MCP-1 plaque levels (pg/mL) (LUMINEX based, two experiments MCP1, and MCP1_pg_ug_2015)
  • MCP-1 serum levels (pg/mL) (OLINK based)
cat("===========================================================================================\n")
===========================================================================================
cat("CREATE BASELINE TABLE\n")
CREATE BASELINE TABLE
# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
                   "Age", "Gender", 
                   "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "restenos", "stenose",
                   "MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6", "IL6_pg_ug_2015", "IL6R_pg_ug_2015",
                   "MCP1", "MCP1_pg_ug_2015")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con

All patients

Showing the baseline table of the whole Athero-Express Biobank.

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
                                      
                                       level                                                                     Overall           Missing
  n                                                                                                                 3791                  
  Hospital % (freq)                    St. Antonius, Nieuwegein                                                     45.8 (1735)     0.0   
                                       UMC Utrecht                                                                  54.2 (2056)           
  ORyear % (freq)                      No data available/missing                                                     0.0 (   0)     0.0   
                                       2002                                                                          2.5 (  94)           
                                       2003                                                                          5.4 ( 204)           
                                       2004                                                                          7.6 ( 289)           
                                       2005                                                                          8.2 ( 309)           
                                       2006                                                                          7.5 ( 285)           
                                       2007                                                                          6.2 ( 234)           
                                       2008                                                                          5.9 ( 223)           
                                       2009                                                                          7.0 ( 267)           
                                       2010                                                                          8.1 ( 307)           
                                       2011                                                                          7.1 ( 269)           
                                       2012                                                                          8.2 ( 312)           
                                       2013                                                                          6.9 ( 262)           
                                       2014                                                                          7.9 ( 299)           
                                       2015                                                                          2.1 (  79)           
                                       2016                                                                          3.3 ( 124)           
                                       2017                                                                          2.2 (  85)           
                                       2018                                                                          2.1 (  80)           
                                       2019                                                                          1.8 (  69)           
  Age (mean (SD))                                                                                                 68.907 (9.322)    0.0   
  Gender % (freq)                      female                                                                       30.6 (1160)     0.0   
                                       male                                                                         69.4 (2631)           
  TC_finalCU (mean (SD))                                                                                         185.220 (81.513)  46.8   
  LDL_finalCU (mean (SD))                                                                                        106.483 (40.683)  54.5   
  HDL_finalCU (mean (SD))                                                                                         46.593 (16.730)  51.1   
  TG_finalCU (mean (SD))                                                                                         154.233 (99.797)  51.8   
  TC_final (mean (SD))                                                                                             4.797 (2.111)   46.8   
  LDL_final (mean (SD))                                                                                            2.758 (1.054)   54.5   
  HDL_final (mean (SD))                                                                                            1.207 (0.433)   51.1   
  TG_final (mean (SD))                                                                                             1.743 (1.128)   51.8   
  hsCRP_plasma (mean (SD))                                                                                        19.250 (206.888) 60.7   
  systolic (mean (SD))                                                                                           150.907 (25.117)  13.5   
  diastoli (mean (SD))                                                                                            79.934 (21.853)  13.5   
  GFR_MDRD (mean (SD))                                                                                            74.849 (24.745)   6.5   
  BMI (mean (SD))                                                                                                 26.336 (4.051)    7.5   
  KDOQI % (freq)                       No data available/missing                                                     0.0 (   0)     6.6   
                                       Normal kidney function                                                       22.1 ( 839)           
                                       CKD 2 (Mild)                                                                 47.2 (1788)           
                                       CKD 3 (Moderate)                                                             21.9 ( 830)           
                                       CKD 4 (Severe)                                                                1.4 (  53)           
                                       CKD 5 (Failure)                                                               0.8 (  32)           
                                       <NA>                                                                          6.6 ( 249)           
  BMI_WHO % (freq)                     No data available/missing                                                     0.0 (   0)     7.5   
                                       Underweight                                                                   1.2 (  44)           
                                       Normal                                                                       35.2 (1335)           
                                       Overweight                                                                   42.0 (1594)           
                                       Obese                                                                        14.1 ( 533)           
                                       <NA>                                                                          7.5 ( 285)           
  SmokerStatus % (freq)                Current smoker                                                               34.5 (1308)     7.4   
                                       Ex-smoker                                                                    47.9 (1814)           
                                       Never smoked                                                                 10.3 ( 389)           
                                       <NA>                                                                          7.4 ( 280)           
  AlcoholUse % (freq)                  No                                                                           32.7 (1238)     5.5   
                                       Yes                                                                          61.9 (2345)           
                                       <NA>                                                                          5.5 ( 208)           
  DiabetesStatus % (freq)              Control (no Diabetes Dx/Med)                                                 72.9 (2764)     1.1   
                                       Diabetes                                                                     26.0 ( 985)           
                                       <NA>                                                                          1.1 (  42)           
  Hypertension.selfreport % (freq)     No data available/missing                                                     0.0 (   0)     4.0   
                                       no                                                                           23.7 ( 899)           
                                       yes                                                                          72.3 (2741)           
                                       <NA>                                                                          4.0 ( 151)           
  Hypertension.selfreportdrug % (freq) No data available/missing                                                     0.0 (   0)     5.5   
                                       no                                                                           28.6 (1085)           
                                       yes                                                                          65.9 (2499)           
                                       <NA>                                                                          5.5 ( 207)           
  Hypertension.composite % (freq)      No data available/missing                                                     0.0 (   0)     1.3   
                                       no                                                                           13.3 ( 504)           
                                       yes                                                                          85.4 (3239)           
                                       <NA>                                                                          1.3 (  48)           
  Hypertension.drugs % (freq)          No data available/missing                                                     0.0 (   0)     1.5   
                                       no                                                                           21.0 ( 797)           
                                       yes                                                                          77.5 (2939)           
                                       <NA>                                                                          1.5 (  55)           
  Med.anticoagulants % (freq)          No data available/missing                                                     0.0 (   0)     1.6   
                                       no                                                                           85.6 (3246)           
                                       yes                                                                          12.8 ( 485)           
                                       <NA>                                                                          1.6 (  60)           
  Med.all.antiplatelet % (freq)        No data available/missing                                                     0.0 (   0)     1.6   
                                       no                                                                           13.7 ( 521)           
                                       yes                                                                          84.7 (3211)           
                                       <NA>                                                                          1.6 (  59)           
  Med.Statin.LLD % (freq)              No data available/missing                                                     0.0 (   0)     1.5   
                                       no                                                                           21.8 ( 826)           
                                       yes                                                                          76.7 (2909)           
                                       <NA>                                                                          1.5 (  56)           
  Stroke_Dx % (freq)                   Missing                                                                       0.0 (   0)     8.1   
                                       No stroke diagnosed                                                          74.4 (2822)           
                                       Stroke diagnosed                                                             17.5 ( 662)           
                                       <NA>                                                                          8.1 ( 307)           
  sympt % (freq)                       missing                                                                      29.1 (1103)     0.0   
                                       Asymptomatic                                                                  8.8 ( 333)           
                                       TIA                                                                          27.4 (1040)           
                                       minor stroke                                                                 12.1 ( 458)           
                                       Major stroke                                                                  7.2 ( 274)           
                                       Amaurosis fugax                                                              10.5 ( 398)           
                                       Four vessel disease                                                           1.1 (  43)           
                                       Vertebrobasilary TIA                                                          0.1 (   5)           
                                       Retinal infarction                                                            1.0 (  37)           
                                       Symptomatic, but aspecific symtoms                                            1.6 (  61)           
                                       Contralateral symptomatic occlusion                                           0.3 (  12)           
                                       retinal infarction                                                            0.2 (   6)           
                                       armclaudication due to occlusion subclavian artery, CEA needed for bypass     0.0 (   1)           
                                       retinal infarction + TIAs                                                     0.0 (   0)           
                                       Ocular ischemic syndrome                                                      0.5 (  18)           
                                       ischemisch glaucoom                                                           0.0 (   0)           
                                       subclavian steal syndrome                                                     0.1 (   2)           
                                       TGA                                                                           0.0 (   0)           
  Symptoms.5G % (freq)                 Asymptomatic                                                                  8.8 ( 333)    29.1   
                                       Ocular                                                                       11.0 ( 416)           
                                       Other                                                                         3.1 ( 119)           
                                       Retinal infarction                                                            1.1 (  43)           
                                       Stroke                                                                       19.3 ( 732)           
                                       TIA                                                                          27.6 (1045)           
                                       <NA>                                                                         29.1 (1103)           
  AsymptSympt % (freq)                 Asymptomatic                                                                  8.8 ( 333)    29.1   
                                       Ocular and others                                                            15.2 ( 578)           
                                       Symptomatic                                                                  46.9 (1777)           
                                       <NA>                                                                         29.1 (1103)           
  AsymptSympt2G % (freq)               Asymptomatic                                                                  8.8 ( 333)    29.1   
                                       Symptomatic                                                                  62.1 (2355)           
                                       <NA>                                                                         29.1 (1103)           
  restenos % (freq)                    missing                                                                       0.0 (   0)     4.0   
                                       de novo                                                                      87.0 (3297)           
                                       restenosis                                                                    8.8 ( 334)           
                                       stenose bij angioseal na PTCA                                                 0.2 (   7)           
                                       <NA>                                                                          4.0 ( 153)           
  stenose % (freq)                     missing                                                                       0.0 (   0)     7.0   
                                       0-49%                                                                         0.7 (  25)           
                                       50-70%                                                                        6.8 ( 256)           
                                       70-90%                                                                       35.6 (1349)           
                                       90-99%                                                                       29.9 (1132)           
                                       100% (Occlusion)                                                             14.8 ( 560)           
                                       NA                                                                            0.1 (   3)           
                                       50-99%                                                                        2.6 (  99)           
                                       70-99%                                                                        2.6 ( 100)           
                                       99                                                                            0.1 (   2)           
                                       <NA>                                                                          7.0 ( 265)           
  MedHx_CVD % (freq)                   No                                                                           34.5 (1309)     0.2   
                                       yes                                                                          65.3 (2475)           
                                       <NA>                                                                          0.2 (   7)           
  CAD_history % (freq)                 Missing                                                                       0.0 (   0)     2.0   
                                       No history CAD                                                               64.1 (2430)           
                                       History CAD                                                                  33.9 (1285)           
                                       <NA>                                                                          2.0 (  76)           
  PAOD % (freq)                        missing/no data                                                               0.0 (   0)     1.6   
                                       no                                                                           55.1 (2088)           
                                       yes                                                                          43.4 (1644)           
                                       <NA>                                                                          1.6 (  59)           
  Peripheral.interv % (freq)           no                                                                           68.0 (2579)     3.0   
                                       yes                                                                          29.0 (1099)           
                                       <NA>                                                                          3.0 ( 113)           
  EP_composite % (freq)                No data available.                                                            0.0 (   0)     7.3   
                                       No composite endpoints                                                       60.6 (2297)           
                                       Composite endpoints                                                          32.1 (1218)           
                                       <NA>                                                                          7.3 ( 276)           
  EP_composite_time (mean (SD))                                                                                    2.266 (1.203)    7.4   
  macmean0 (mean (SD))                                                                                             0.656 (1.154)   32.4   
  smcmean0 (mean (SD))                                                                                             2.291 (6.620)   32.4   
  Macrophages.bin % (freq)             no/minor                                                                     42.3 (1602)    25.7   
                                       moderate/heavy                                                               32.0 (1215)           
                                       <NA>                                                                         25.7 ( 974)           
  SMC.bin % (freq)                     no/minor                                                                     22.9 ( 870)    25.3   
                                       moderate/heavy                                                               51.8 (1962)           
                                       <NA>                                                                         25.3 ( 959)           
  neutrophils (mean (SD))                                                                                        162.985 (490.469) 91.0   
  Mast_cells_plaque (mean (SD))                                                                                  165.663 (163.421) 93.0   
  IPH.bin % (freq)                     no                                                                           32.3 (1223)    24.8   
                                       yes                                                                          42.9 (1628)           
                                       <NA>                                                                         24.8 ( 940)           
  vessel_density_averaged (mean (SD))                                                                              8.030 (6.348)   48.0   
  Calc.bin % (freq)                    no/minor                                                                     37.9 (1437)    24.7   
                                       moderate/heavy                                                               37.4 (1416)           
                                       <NA>                                                                         24.7 ( 938)           
  Collagen.bin % (freq)                no/minor                                                                     14.2 ( 540)    25.2   
                                       moderate/heavy                                                               60.6 (2297)           
                                       <NA>                                                                         25.2 ( 954)           
  Fat.bin_10 % (freq)                   <10%                                                                        32.3 (1226)    24.7   
                                        >10%                                                                        42.9 (1628)           
                                       <NA>                                                                         24.7 ( 937)           
  Fat.bin_40 % (freq)                  <40%                                                                         60.0 (2274)    24.7   
                                       >40%                                                                         15.3 ( 580)           
                                       <NA>                                                                         24.7 ( 937)           
  OverallPlaquePhenotype % (freq)      atheromatous                                                                 14.5 ( 550)    25.3   
                                       fibroatheromatous                                                            22.2 ( 841)           
                                       fibrous                                                                      38.0 (1439)           
                                       <NA>                                                                         25.3 ( 961)           
  IL6 (mean (SD))                                                                                                 94.451 (278.490) 84.5   
  IL6_pg_ug_2015 (mean (SD))                                                                                       0.135 (0.541)   67.2   
  IL6R_pg_ug_2015 (mean (SD))                                                                                      0.212 (0.251)   67.1   
  MCP1 (mean (SD))                                                                                               130.926 (118.422) 83.7   
  MCP1_pg_ug_2015 (mean (SD))                                                                                      0.596 (0.880)   65.5   

CEA patients

Showing the baseline table of the CEA patients in the Athero-Express Biobank.

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
                                      
                                       level                                                                     Overall           Missing
  n                                                                                                                 2421                  
  Hospital % (freq)                    St. Antonius, Nieuwegein                                                     39.2 ( 948)     0.0   
                                       UMC Utrecht                                                                  60.8 (1473)           
  ORyear % (freq)                      No data available/missing                                                     0.0 (   0)     0.0   
                                       2002                                                                          3.3 (  81)           
                                       2003                                                                          6.5 ( 157)           
                                       2004                                                                          7.8 ( 190)           
                                       2005                                                                          7.6 ( 185)           
                                       2006                                                                          7.6 ( 183)           
                                       2007                                                                          6.3 ( 152)           
                                       2008                                                                          5.7 ( 138)           
                                       2009                                                                          7.5 ( 181)           
                                       2010                                                                          6.6 ( 159)           
                                       2011                                                                          6.7 ( 163)           
                                       2012                                                                          7.3 ( 176)           
                                       2013                                                                          6.2 ( 149)           
                                       2014                                                                          6.7 ( 163)           
                                       2015                                                                          3.1 (  76)           
                                       2016                                                                          3.5 (  85)           
                                       2017                                                                          2.7 (  65)           
                                       2018                                                                          2.7 (  66)           
                                       2019                                                                          2.1 (  52)           
  Age (mean (SD))                                                                                                 69.105 (9.302)    0.0   
  Gender % (freq)                      female                                                                       30.5 ( 738)     0.0   
                                       male                                                                         69.5 (1683)           
  TC_finalCU (mean (SD))                                                                                         184.803 (56.262)  38.0   
  LDL_finalCU (mean (SD))                                                                                        108.420 (41.744)  45.6   
  HDL_finalCU (mean (SD))                                                                                         46.435 (17.005)  41.7   
  TG_finalCU (mean (SD))                                                                                         151.216 (91.277)  42.8   
  TC_final (mean (SD))                                                                                             4.786 (1.457)   38.0   
  LDL_final (mean (SD))                                                                                            2.808 (1.081)   45.6   
  HDL_final (mean (SD))                                                                                            1.203 (0.440)   41.7   
  TG_final (mean (SD))                                                                                             1.709 (1.031)   42.8   
  hsCRP_plasma (mean (SD))                                                                                        19.914 (231.655) 53.0   
  systolic (mean (SD))                                                                                           152.419 (25.166)  11.3   
  diastoli (mean (SD))                                                                                            81.318 (25.188)  11.3   
  GFR_MDRD (mean (SD))                                                                                            73.121 (21.152)   5.4   
  BMI (mean (SD))                                                                                                 26.488 (3.977)    5.9   
  KDOQI % (freq)                       No data available/missing                                                     0.0 (   0)     5.5   
                                       Normal kidney function                                                       19.1 ( 462)           
                                       CKD 2 (Mild)                                                                 50.9 (1232)           
                                       CKD 3 (Moderate)                                                             22.8 ( 553)           
                                       CKD 4 (Severe)                                                                1.3 (  32)           
                                       CKD 5 (Failure)                                                               0.4 (  10)           
                                       <NA>                                                                          5.5 ( 132)           
  BMI_WHO % (freq)                     No data available/missing                                                     0.0 (   0)     5.9   
                                       Underweight                                                                   1.0 (  24)           
                                       Normal                                                                       35.1 ( 850)           
                                       Overweight                                                                   43.4 (1051)           
                                       Obese                                                                        14.5 ( 352)           
                                       <NA>                                                                          5.9 ( 144)           
  SmokerStatus % (freq)                Current smoker                                                               33.2 ( 803)     5.9   
                                       Ex-smoker                                                                    48.0 (1163)           
                                       Never smoked                                                                 12.9 ( 313)           
                                       <NA>                                                                          5.9 ( 142)           
  AlcoholUse % (freq)                  No                                                                           34.5 ( 835)     4.0   
                                       Yes                                                                          61.5 (1488)           
                                       <NA>                                                                          4.0 (  98)           
  DiabetesStatus % (freq)              Control (no Diabetes Dx/Med)                                                 75.2 (1820)     1.1   
                                       Diabetes                                                                     23.7 ( 574)           
                                       <NA>                                                                          1.1 (  27)           
  Hypertension.selfreport % (freq)     No data available/missing                                                     0.0 (   0)     3.2   
                                       no                                                                           24.3 ( 589)           
                                       yes                                                                          72.4 (1754)           
                                       <NA>                                                                          3.2 (  78)           
  Hypertension.selfreportdrug % (freq) No data available/missing                                                     0.0 (   0)     4.4   
                                       no                                                                           29.9 ( 725)           
                                       yes                                                                          65.6 (1589)           
                                       <NA>                                                                          4.4 ( 107)           
  Hypertension.composite % (freq)      No data available/missing                                                     0.0 (   0)     1.2   
                                       no                                                                           14.6 ( 353)           
                                       yes                                                                          84.3 (2040)           
                                       <NA>                                                                          1.2 (  28)           
  Hypertension.drugs % (freq)          No data available/missing                                                     0.0 (   0)     1.4   
                                       no                                                                           23.3 ( 565)           
                                       yes                                                                          75.3 (1823)           
                                       <NA>                                                                          1.4 (  33)           
  Med.anticoagulants % (freq)          No data available/missing                                                     0.0 (   0)     1.6   
                                       no                                                                           87.3 (2114)           
                                       yes                                                                          11.1 ( 269)           
                                       <NA>                                                                          1.6 (  38)           
  Med.all.antiplatelet % (freq)        No data available/missing                                                     0.0 (   0)     1.5   
                                       no                                                                           12.2 ( 295)           
                                       yes                                                                          86.3 (2090)           
                                       <NA>                                                                          1.5 (  36)           
  Med.Statin.LLD % (freq)              No data available/missing                                                     0.0 (   0)     1.4   
                                       no                                                                           20.3 ( 491)           
                                       yes                                                                          78.3 (1896)           
                                       <NA>                                                                          1.4 (  34)           
  Stroke_Dx % (freq)                   Missing                                                                       0.0 (   0)     6.9   
                                       No stroke diagnosed                                                          71.5 (1731)           
                                       Stroke diagnosed                                                             21.6 ( 524)           
                                       <NA>                                                                          6.9 ( 166)           
  sympt % (freq)                       missing                                                                       0.0 (   0)     0.0   
                                       Asymptomatic                                                                 11.2 ( 270)           
                                       TIA                                                                          39.7 ( 961)           
                                       minor stroke                                                                 16.8 ( 407)           
                                       Major stroke                                                                  9.8 ( 238)           
                                       Amaurosis fugax                                                              15.7 ( 379)           
                                       Four vessel disease                                                           1.6 (  38)           
                                       Vertebrobasilary TIA                                                          0.2 (   5)           
                                       Retinal infarction                                                            1.4 (  34)           
                                       Symptomatic, but aspecific symtoms                                            2.2 (  53)           
                                       Contralateral symptomatic occlusion                                           0.5 (  11)           
                                       retinal infarction                                                            0.2 (   6)           
                                       armclaudication due to occlusion subclavian artery, CEA needed for bypass     0.0 (   1)           
                                       retinal infarction + TIAs                                                     0.0 (   0)           
                                       Ocular ischemic syndrome                                                      0.7 (  16)           
                                       ischemisch glaucoom                                                           0.0 (   0)           
                                       subclavian steal syndrome                                                     0.1 (   2)           
                                       TGA                                                                           0.0 (   0)           
  Symptoms.5G % (freq)                 Asymptomatic                                                                 11.2 ( 270)     0.0   
                                       Ocular                                                                       16.3 ( 395)           
                                       Other                                                                         4.3 ( 105)           
                                       Retinal infarction                                                            1.7 (  40)           
                                       Stroke                                                                       26.6 ( 645)           
                                       TIA                                                                          39.9 ( 966)           
  AsymptSympt % (freq)                 Asymptomatic                                                                 11.2 ( 270)     0.0   
                                       Ocular and others                                                            22.3 ( 540)           
                                       Symptomatic                                                                  66.5 (1611)           
  AsymptSympt2G % (freq)               Asymptomatic                                                                 11.2 ( 270)     0.0   
                                       Symptomatic                                                                  88.8 (2151)           
  restenos % (freq)                    missing                                                                       0.0 (   0)     1.4   
                                       de novo                                                                      93.7 (2268)           
                                       restenosis                                                                    4.9 ( 118)           
                                       stenose bij angioseal na PTCA                                                 0.0 (   0)           
                                       <NA>                                                                          1.4 (  35)           
  stenose % (freq)                     missing                                                                       0.0 (   0)     2.0   
                                       0-49%                                                                         0.5 (  13)           
                                       50-70%                                                                        7.8 ( 189)           
                                       70-90%                                                                       46.6 (1127)           
                                       90-99%                                                                       38.3 ( 927)           
                                       100% (Occlusion)                                                              1.3 (  31)           
                                       NA                                                                            0.0 (   1)           
                                       50-99%                                                                        0.6 (  15)           
                                       70-99%                                                                        2.8 (  68)           
                                       99                                                                            0.1 (   2)           
                                       <NA>                                                                          2.0 (  48)           
  MedHx_CVD % (freq)                   No                                                                           36.8 ( 892)     0.0   
                                       yes                                                                          63.2 (1529)           
  CAD_history % (freq)                 Missing                                                                       0.0 (   0)     1.9   
                                       No history CAD                                                               66.8 (1618)           
                                       History CAD                                                                  31.2 ( 756)           
                                       <NA>                                                                          1.9 (  47)           
  PAOD % (freq)                        missing/no data                                                               0.0 (   0)     2.0   
                                       no                                                                           77.5 (1876)           
                                       yes                                                                          20.5 ( 497)           
                                       <NA>                                                                          2.0 (  48)           
  Peripheral.interv % (freq)           no                                                                           77.2 (1868)     2.9   
                                       yes                                                                          19.9 ( 482)           
                                       <NA>                                                                          2.9 (  71)           
  EP_composite % (freq)                No data available.                                                            0.0 (   0)     5.0   
                                       No composite endpoints                                                       70.6 (1709)           
                                       Composite endpoints                                                          24.4 ( 590)           
                                       <NA>                                                                          5.0 ( 122)           
  EP_composite_time (mean (SD))                                                                                    2.479 (1.109)    5.2   
  macmean0 (mean (SD))                                                                                             0.768 (1.184)   29.7   
  smcmean0 (mean (SD))                                                                                             1.985 (2.381)   29.9   
  Macrophages.bin % (freq)             no/minor                                                                     34.9 ( 846)    24.1   
                                       moderate/heavy                                                               40.9 ( 991)           
                                       <NA>                                                                         24.1 ( 584)           
  SMC.bin % (freq)                     no/minor                                                                     24.9 ( 602)    23.8   
                                       moderate/heavy                                                               51.3 (1242)           
                                       <NA>                                                                         23.8 ( 577)           
  neutrophils (mean (SD))                                                                                        147.151 (419.998) 87.4   
  Mast_cells_plaque (mean (SD))                                                                                  164.488 (163.771) 90.0   
  IPH.bin % (freq)                     no                                                                           30.7 ( 744)    23.5   
                                       yes                                                                          45.8 (1108)           
                                       <NA>                                                                         23.5 ( 569)           
  vessel_density_averaged (mean (SD))                                                                              8.318 (6.388)   35.1   
  Calc.bin % (freq)                    no/minor                                                                     41.6 (1006)    23.4   
                                       moderate/heavy                                                               35.1 ( 849)           
                                       <NA>                                                                         23.4 ( 566)           
  Collagen.bin % (freq)                no/minor                                                                     15.8 ( 382)    23.6   
                                       moderate/heavy                                                               60.6 (1467)           
                                       <NA>                                                                         23.6 ( 572)           
  Fat.bin_10 % (freq)                   <10%                                                                        22.4 ( 542)    23.3   
                                        >10%                                                                        54.3 (1314)           
                                       <NA>                                                                         23.3 ( 565)           
  Fat.bin_40 % (freq)                  <40%                                                                         56.2 (1360)    23.3   
                                       >40%                                                                         20.5 ( 496)           
                                       <NA>                                                                         23.3 ( 565)           
  OverallPlaquePhenotype % (freq)      atheromatous                                                                 19.8 ( 480)    23.7   
                                       fibroatheromatous                                                            27.8 ( 672)           
                                       fibrous                                                                      28.7 ( 695)           
                                       <NA>                                                                         23.7 ( 574)           
  IL6 (mean (SD))                                                                                                 98.812 (292.457) 78.2   
  IL6_pg_ug_2015 (mean (SD))                                                                                       0.138 (0.556)   52.5   
  IL6R_pg_ug_2015 (mean (SD))                                                                                      0.212 (0.251)   52.4   
  MCP1 (mean (SD))                                                                                               135.763 (120.028) 76.7   
  MCP1_pg_ug_2015 (mean (SD))                                                                                      0.612 (0.905)   50.6   

CEA patients with MCP1_pg_ug_2015

Showing the baseline table of the CEA patients in the Athero-Express Biobank with MCP1_pg_ug_2015.

AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015))

AEDB.CEA.subset.AsymptSympt.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]
                                      Stratified by AsymptSympt2G
                                       level                                                                     Asymptomatic      Symptomatic       p      test Missing
  n                                                                                                                  131              1065                              
  Hospital % (freq)                    St. Antonius, Nieuwegein                                                     50.4 ( 66)        46.6 ( 496)     0.464       0.0   
                                       UMC Utrecht                                                                  49.6 ( 65)        53.4 ( 569)                       
  ORyear % (freq)                      No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       2002                                                                         10.7 ( 14)         3.9 (  42)                       
                                       2003                                                                          7.6 ( 10)         9.4 ( 100)                       
                                       2004                                                                         17.6 ( 23)        11.5 ( 123)                       
                                       2005                                                                          9.9 ( 13)        11.2 ( 119)                       
                                       2006                                                                         10.7 ( 14)        10.2 ( 109)                       
                                       2007                                                                         11.5 ( 15)        10.5 ( 112)                       
                                       2008                                                                          7.6 ( 10)         7.4 (  79)                       
                                       2009                                                                          7.6 ( 10)         8.4 (  89)                       
                                       2010                                                                          5.3 (  7)         7.6 (  81)                       
                                       2011                                                                          6.1 (  8)         9.5 ( 101)                       
                                       2012                                                                          5.3 (  7)         8.3 (  88)                       
                                       2013                                                                          0.0 (  0)         2.0 (  21)                       
                                       2014                                                                          0.0 (  0)         0.1 (   1)                       
                                       2015                                                                          0.0 (  0)         0.0 (   0)                       
                                       2016                                                                          0.0 (  0)         0.0 (   0)                       
                                       2017                                                                          0.0 (  0)         0.0 (   0)                       
                                       2018                                                                          0.0 (  0)         0.0 (   0)                       
                                       2019                                                                          0.0 (  0)         0.0 (   0)                       
  Age (mean (SD))                                                                                                 66.237 (9.184)    68.941 (9.119)    0.001       0.0   
  Gender % (freq)                      female                                                                       23.7 ( 31)        31.4 ( 334)     0.088       0.0   
                                       male                                                                         76.3 (100)        68.6 ( 731)                       
  TC_finalCU (mean (SD))                                                                                         175.987 (47.184)  183.420 (48.377)   0.180      33.4   
  LDL_finalCU (mean (SD))                                                                                        102.781 (38.324)  109.247 (41.008)   0.191      39.6   
  HDL_finalCU (mean (SD))                                                                                         43.701 (14.754)   45.814 (18.526)   0.317      36.4   
  TG_finalCU (mean (SD))                                                                                         157.650 (89.246)  145.238 (84.872)   0.211      36.0   
  TC_final (mean (SD))                                                                                             4.558 (1.222)     4.751 (1.253)    0.180      33.4   
  LDL_final (mean (SD))                                                                                            2.662 (0.993)     2.829 (1.062)    0.191      39.6   
  HDL_final (mean (SD))                                                                                            1.132 (0.382)     1.187 (0.480)    0.317      36.4   
  TG_final (mean (SD))                                                                                             1.781 (1.008)     1.641 (0.959)    0.211      36.0   
  hsCRP_plasma (mean (SD))                                                                                         5.688 (19.440)   16.588 (113.882)  0.379      38.8   
  systolic (mean (SD))                                                                                           153.577 (24.327)  155.822 (26.180)   0.390      14.0   
  diastoli (mean (SD))                                                                                            80.622 (13.225)   82.894 (13.581)   0.095      14.0   
  GFR_MDRD (mean (SD))                                                                                            71.026 (20.424)   71.879 (20.071)   0.653       3.5   
  BMI (mean (SD))                                                                                                 26.623 (3.391)    26.321 (3.748)    0.383       4.2   
  KDOQI % (freq)                       No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         3.6   
                                       Normal kidney function                                                       17.6 ( 23)        17.3 ( 184)                       
                                       CKD 2 (Mild)                                                                 49.6 ( 65)        53.2 ( 567)                       
                                       CKD 3 (Moderate)                                                             28.2 ( 37)        24.3 ( 259)                       
                                       CKD 4 (Severe)                                                                0.0 (  0)         1.2 (  13)                       
                                       CKD 5 (Failure)                                                               0.8 (  1)         0.4 (   4)                       
                                       <NA>                                                                          3.8 (  5)         3.6 (  38)                       
  BMI_WHO % (freq)                     No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         4.3   
                                       Underweight                                                                   0.8 (  1)         0.9 (  10)                       
                                       Normal                                                                       32.8 ( 43)        35.6 ( 379)                       
                                       Overweight                                                                   51.1 ( 67)        46.1 ( 491)                       
                                       Obese                                                                        13.0 ( 17)        12.8 ( 136)                       
                                       <NA>                                                                          2.3 (  3)         4.6 (  49)                       
  SmokerStatus % (freq)                Current smoker                                                               30.5 ( 40)        36.2 ( 385)     0.077       3.8   
                                       Ex-smoker                                                                    57.3 ( 75)        45.6 ( 486)                       
                                       Never smoked                                                                  9.9 ( 13)        14.3 ( 152)                       
                                       <NA>                                                                          2.3 (  3)         3.9 (  42)                       
  AlcoholUse % (freq)                  No                                                                           38.2 ( 50)        33.3 ( 355)     0.359       4.0   
                                       Yes                                                                          59.5 ( 78)        62.4 ( 665)                       
                                       <NA>                                                                          2.3 (  3)         4.2 (  45)                       
  DiabetesStatus % (freq)              Control (no Diabetes Dx/Med)                                                 76.3 (100)        77.4 ( 824)     0.876       0.0   
                                       Diabetes                                                                     23.7 ( 31)        22.6 ( 241)                       
  Hypertension.selfreport % (freq)     No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         2.0   
                                       no                                                                           23.7 ( 31)        26.6 ( 283)                       
                                       yes                                                                          75.6 ( 99)        71.3 ( 759)                       
                                       <NA>                                                                          0.8 (  1)         2.2 (  23)                       
  Hypertension.selfreportdrug % (freq) No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         2.7   
                                       no                                                                           30.5 ( 40)        32.9 ( 350)                       
                                       yes                                                                          67.9 ( 89)        64.3 ( 685)                       
                                       <NA>                                                                          1.5 (  2)         2.8 (  30)                       
  Hypertension.composite % (freq)      No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       no                                                                            9.9 ( 13)        14.3 ( 152)                       
                                       yes                                                                          90.1 (118)        85.7 ( 913)                       
  Hypertension.drugs % (freq)          No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.2   
                                       no                                                                           14.5 ( 19)        23.3 ( 248)                       
                                       yes                                                                          85.5 (112)        76.5 ( 815)                       
                                       <NA>                                                                          0.0 (  0)         0.2 (   2)                       
  Med.anticoagulants % (freq)          No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.2   
                                       no                                                                           89.3 (117)        88.0 ( 937)                       
                                       yes                                                                          10.7 ( 14)        11.8 ( 126)                       
                                       <NA>                                                                          0.0 (  0)         0.2 (   2)                       
  Med.all.antiplatelet % (freq)        No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.4   
                                       no                                                                            6.1 (  8)        11.0 ( 117)                       
                                       yes                                                                          93.1 (122)        88.6 ( 944)                       
                                       <NA>                                                                          0.8 (  1)         0.4 (   4)                       
  Med.Statin.LLD % (freq)              No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.2   
                                       no                                                                           15.3 ( 20)        22.7 ( 242)                       
                                       yes                                                                          84.7 (111)        77.1 ( 821)                       
                                       <NA>                                                                          0.0 (  0)         0.2 (   2)                       
  Stroke_Dx % (freq)                   Missing                                                                       0.0 (  0)         0.0 (   0)     NaN         5.4   
                                       No stroke diagnosed                                                          80.2 (105)        75.2 ( 801)                       
                                       Stroke diagnosed                                                             14.5 ( 19)        19.4 ( 207)                       
                                       <NA>                                                                          5.3 (  7)         5.4 (  57)                       
  sympt % (freq)                       missing                                                                       0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       Asymptomatic                                                                100.0 (131)         0.0 (   0)                       
                                       TIA                                                                           0.0 (  0)        46.4 ( 494)                       
                                       minor stroke                                                                  0.0 (  0)        16.7 ( 178)                       
                                       Major stroke                                                                  0.0 (  0)        12.2 ( 130)                       
                                       Amaurosis fugax                                                               0.0 (  0)        17.2 ( 183)                       
                                       Four vessel disease                                                           0.0 (  0)         2.2 (  23)                       
                                       Vertebrobasilary TIA                                                          0.0 (  0)         0.2 (   2)                       
                                       Retinal infarction                                                            0.0 (  0)         1.4 (  15)                       
                                       Symptomatic, but aspecific symtoms                                            0.0 (  0)         2.7 (  29)                       
                                       Contralateral symptomatic occlusion                                           0.0 (  0)         0.6 (   6)                       
                                       retinal infarction                                                            0.0 (  0)         0.3 (   3)                       
                                       armclaudication due to occlusion subclavian artery, CEA needed for bypass     0.0 (  0)         0.1 (   1)                       
                                       retinal infarction + TIAs                                                     0.0 (  0)         0.0 (   0)                       
                                       Ocular ischemic syndrome                                                      0.0 (  0)         0.1 (   1)                       
                                       ischemisch glaucoom                                                           0.0 (  0)         0.0 (   0)                       
                                       subclavian steal syndrome                                                     0.0 (  0)         0.0 (   0)                       
                                       TGA                                                                           0.0 (  0)         0.0 (   0)                       
  Symptoms.5G % (freq)                 Asymptomatic                                                                100.0 (131)         0.0 (   0)    <0.001       0.0   
                                       Ocular                                                                        0.0 (  0)        17.3 ( 184)                       
                                       Other                                                                         0.0 (  0)         5.5 (  59)                       
                                       Retinal infarction                                                            0.0 (  0)         1.7 (  18)                       
                                       Stroke                                                                        0.0 (  0)        28.9 ( 308)                       
                                       TIA                                                                           0.0 (  0)        46.6 ( 496)                       
  AsymptSympt % (freq)                 Asymptomatic                                                                100.0 (131)         0.0 (   0)    <0.001       0.0   
                                       Ocular and others                                                             0.0 (  0)        24.5 ( 261)                       
                                       Symptomatic                                                                   0.0 (  0)        75.5 ( 804)                       
  AsymptSympt2G % (freq)               Asymptomatic                                                                100.0 (131)         0.0 (   0)    <0.001       0.0   
                                       Symptomatic                                                                   0.0 (  0)       100.0 (1065)                       
  restenos % (freq)                    missing                                                                       0.0 (  0)         0.0 (   0)     NaN         2.3   
                                       de novo                                                                      93.9 (123)        94.7 (1009)                       
                                       restenosis                                                                    2.3 (  3)         3.2 (  34)                       
                                       stenose bij angioseal na PTCA                                                 0.0 (  0)         0.0 (   0)                       
                                       <NA>                                                                          3.8 (  5)         2.1 (  22)                       
  stenose % (freq)                     missing                                                                       0.0 (  0)         0.0 (   0)     NaN         3.2   
                                       0-49%                                                                         0.0 (  0)         0.6 (   6)                       
                                       50-70%                                                                        3.1 (  4)         6.4 (  68)                       
                                       70-90%                                                                       51.1 ( 67)        44.6 ( 475)                       
                                       90-99%                                                                       41.2 ( 54)        42.7 ( 455)                       
                                       100% (Occlusion)                                                              0.0 (  0)         0.9 (  10)                       
                                       NA                                                                            0.0 (  0)         0.0 (   0)                       
                                       50-99%                                                                        0.8 (  1)         0.4 (   4)                       
                                       70-99%                                                                        0.0 (  0)         1.3 (  14)                       
                                       99                                                                            0.0 (  0)         0.0 (   0)                       
                                       <NA>                                                                          3.8 (  5)         3.1 (  33)                       
  MedHx_CVD % (freq)                   No                                                                           38.9 ( 51)        36.9 ( 393)     0.720       0.0   
                                       yes                                                                          61.1 ( 80)        63.1 ( 672)                       
  CAD_history % (freq)                 Missing                                                                       0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       No history CAD                                                               61.8 ( 81)        69.9 ( 744)                       
                                       History CAD                                                                  38.2 ( 50)        30.1 ( 321)                       
  PAOD % (freq)                        missing/no data                                                               0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       no                                                                           74.0 ( 97)        79.5 ( 847)                       
                                       yes                                                                          26.0 ( 34)        20.5 ( 218)                       
  Peripheral.interv % (freq)           no                                                                           74.0 ( 97)        82.5 ( 879)     0.042       0.3   
                                       yes                                                                          26.0 ( 34)        17.2 ( 183)                       
                                       <NA>                                                                          0.0 (  0)         0.3 (   3)                       
  EP_composite % (freq)                No data available.                                                            0.0 (  0)         0.0 (   0)     NaN         0.8   
                                       No composite endpoints                                                       67.2 ( 88)        74.3 ( 791)                       
                                       Composite endpoints                                                          32.8 ( 43)        24.9 ( 265)                       
                                       <NA>                                                                          0.0 (  0)         0.8 (   9)                       
  EP_composite_time (mean (SD))                                                                                    2.614 (0.931)     2.613 (1.095)    0.992       0.9   
  macmean0 (mean (SD))                                                                                             0.837 (1.088)     0.781 (1.231)    0.623       2.3   
  smcmean0 (mean (SD))                                                                                             2.152 (1.861)     1.904 (2.222)    0.223       2.7   
  Macrophages.bin % (freq)             no/minor                                                                     48.9 ( 64)        47.4 ( 505)     0.583       1.9   
                                       moderate/heavy                                                               50.4 ( 66)        50.5 ( 538)                       
                                       <NA>                                                                          0.8 (  1)         2.1 (  22)                       
  SMC.bin % (freq)                     no/minor                                                                     22.9 ( 30)        32.2 ( 343)     0.085       1.8   
                                       moderate/heavy                                                               75.6 ( 99)        65.9 ( 702)                       
                                       <NA>                                                                          1.5 (  2)         1.9 (  20)                       
  neutrophils (mean (SD))                                                                                        157.643 (507.380) 172.872 (477.038)  0.876      81.9   
  Mast_cells_plaque (mean (SD))                                                                                  111.400 (112.037) 183.284 (180.156)  0.056      86.1   
  IPH.bin % (freq)                     no                                                                           41.2 ( 54)        37.9 ( 404)     0.561       1.7   
                                       yes                                                                          58.0 ( 76)        60.3 ( 642)                       
                                       <NA>                                                                          0.8 (  1)         1.8 (  19)                       
  vessel_density_averaged (mean (SD))                                                                              8.608 (6.547)     8.408 (6.469)    0.750       8.7   
 [ reached getOption("max.print") -- omitted 21 rows ]

CEA patients with MCP1_pg_ug_2015 and MCP1

Showing the baseline table of the CEA patients in the Athero-Express Biobank with MCP1_pg_ug_2015 and MCP1.


AEDB.CEA.subset.combo <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) | !is.na(MCP1))

AEDB.CEA.subset.combo.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.combo, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]
                                      Stratified by AsymptSympt2G
                                       level                                                                     Asymptomatic      Symptomatic       p      test Missing
  n                                                                                                                  161              1165                              
  Hospital % (freq)                    St. Antonius, Nieuwegein                                                     52.2 ( 84)        47.0 ( 547)     0.246       0.0   
                                       UMC Utrecht                                                                  47.8 ( 77)        53.0 ( 618)                       
  ORyear % (freq)                      No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       2002                                                                         10.6 ( 17)         4.8 (  56)                       
                                       2003                                                                         11.8 ( 19)        10.6 ( 124)                       
                                       2004                                                                         19.9 ( 32)        12.2 ( 142)                       
                                       2005                                                                         13.7 ( 22)        13.3 ( 155)                       
                                       2006                                                                          8.7 ( 14)        10.0 ( 116)                       
                                       2007                                                                          9.3 ( 15)         9.6 ( 112)                       
                                       2008                                                                          6.2 ( 10)         6.8 (  79)                       
                                       2009                                                                          6.2 ( 10)         7.6 (  89)                       
                                       2010                                                                          4.3 (  7)         7.0 (  81)                       
                                       2011                                                                          5.0 (  8)         8.7 ( 101)                       
                                       2012                                                                          4.3 (  7)         7.6 (  88)                       
                                       2013                                                                          0.0 (  0)         1.8 (  21)                       
                                       2014                                                                          0.0 (  0)         0.1 (   1)                       
                                       2015                                                                          0.0 (  0)         0.0 (   0)                       
                                       2016                                                                          0.0 (  0)         0.0 (   0)                       
                                       2017                                                                          0.0 (  0)         0.0 (   0)                       
                                       2018                                                                          0.0 (  0)         0.0 (   0)                       
                                       2019                                                                          0.0 (  0)         0.0 (   0)                       
  Age (mean (SD))                                                                                                 65.901 (9.051)    68.788 (9.081)   <0.001       0.0   
  Gender % (freq)                      female                                                                       23.0 ( 37)        30.4 ( 354)     0.066       0.0   
                                       male                                                                         77.0 (124)        69.6 ( 811)                       
  TC_finalCU (mean (SD))                                                                                         179.199 (45.274)  183.983 (48.290)   0.331      32.7   
  LDL_finalCU (mean (SD))                                                                                        104.132 (37.590)  109.642 (41.227)   0.215      39.8   
  HDL_finalCU (mean (SD))                                                                                         44.749 (14.890)   45.808 (18.231)   0.568      36.1   
  TG_finalCU (mean (SD))                                                                                         158.699 (87.584)  145.942 (83.223)   0.143      35.6   
  TC_final (mean (SD))                                                                                             4.641 (1.173)     4.765 (1.251)    0.331      32.7   
  LDL_final (mean (SD))                                                                                            2.697 (0.974)     2.840 (1.068)    0.215      39.8   
  HDL_final (mean (SD))                                                                                            1.159 (0.386)     1.186 (0.472)    0.568      36.1   
  TG_final (mean (SD))                                                                                             1.793 (0.990)     1.649 (0.940)    0.143      35.6   
  hsCRP_plasma (mean (SD))                                                                                         6.846 (21.838)   16.213 (110.899)  0.393      40.6   
  systolic (mean (SD))                                                                                           152.838 (24.600)  155.742 (26.411)   0.225      13.5   
  diastoli (mean (SD))                                                                                            80.824 (12.855)   82.873 (13.549)   0.096      13.5   
  GFR_MDRD (mean (SD))                                                                                            70.440 (19.793)   71.901 (20.142)   0.396       3.5   
  BMI (mean (SD))                                                                                                 26.626 (3.572)    26.350 (3.768)    0.389       4.4   
  KDOQI % (freq)                       No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         3.5   
                                       Normal kidney function                                                       14.9 ( 24)        17.4 ( 203)                       
                                       CKD 2 (Mild)                                                                 50.9 ( 82)        53.4 ( 622)                       
                                       CKD 3 (Moderate)                                                             29.8 ( 48)        23.9 ( 279)                       
                                       CKD 4 (Severe)                                                                0.0 (  0)         1.3 (  15)                       
                                       CKD 5 (Failure)                                                               0.6 (  1)         0.4 (   5)                       
                                       <NA>                                                                          3.7 (  6)         3.5 (  41)                       
  BMI_WHO % (freq)                     No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         4.6   
                                       Underweight                                                                   1.2 (  2)         0.9 (  11)                       
                                       Normal                                                                       32.3 ( 52)        35.5 ( 414)                       
                                       Overweight                                                                   49.7 ( 80)        45.6 ( 531)                       
                                       Obese                                                                        13.7 ( 22)        13.1 ( 153)                       
                                       <NA>                                                                          3.1 (  5)         4.8 (  56)                       
  SmokerStatus % (freq)                Current smoker                                                               29.2 ( 47)        36.0 ( 419)     0.072       4.0   
                                       Ex-smoker                                                                    56.5 ( 91)        45.7 ( 532)                       
                                       Never smoked                                                                 11.8 ( 19)        14.2 ( 165)                       
                                       <NA>                                                                          2.5 (  4)         4.2 (  49)                       
  AlcoholUse % (freq)                  No                                                                           38.5 ( 62)        33.6 ( 392)     0.223       3.8   
                                       Yes                                                                          59.6 ( 96)        62.2 ( 725)                       
                                       <NA>                                                                          1.9 (  3)         4.1 (  48)                       
  DiabetesStatus % (freq)              Control (no Diabetes Dx/Med)                                                 78.3 (126)        77.3 ( 900)     0.852       0.0   
                                       Diabetes                                                                     21.7 ( 35)        22.7 ( 265)                       
  Hypertension.selfreport % (freq)     No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         1.9   
                                       no                                                                           25.5 ( 41)        26.5 ( 309)                       
                                       yes                                                                          73.9 (119)        71.4 ( 832)                       
                                       <NA>                                                                          0.6 (  1)         2.1 (  24)                       
  Hypertension.selfreportdrug % (freq) No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         2.4   
                                       no                                                                           32.3 ( 52)        32.9 ( 383)                       
                                       yes                                                                          66.5 (107)        64.5 ( 752)                       
                                       <NA>                                                                          1.2 (  2)         2.6 (  30)                       
  Hypertension.composite % (freq)      No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       no                                                                           11.2 ( 18)        14.1 ( 164)                       
                                       yes                                                                          88.8 (143)        85.9 (1001)                       
  Hypertension.drugs % (freq)          No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.2   
                                       no                                                                           15.5 ( 25)        22.7 ( 265)                       
                                       yes                                                                          83.9 (135)        77.1 ( 898)                       
                                       <NA>                                                                          0.6 (  1)         0.2 (   2)                       
  Med.anticoagulants % (freq)          No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.2   
                                       no                                                                           89.4 (144)        88.0 (1025)                       
                                       yes                                                                           9.9 ( 16)        11.8 ( 138)                       
                                       <NA>                                                                          0.6 (  1)         0.2 (   2)                       
  Med.all.antiplatelet % (freq)        No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.5   
                                       no                                                                            6.2 ( 10)        10.8 ( 126)                       
                                       yes                                                                          92.5 (149)        88.8 (1035)                       
                                       <NA>                                                                          1.2 (  2)         0.3 (   4)                       
  Med.Statin.LLD % (freq)              No data available/missing                                                     0.0 (  0)         0.0 (   0)     NaN         0.2   
                                       no                                                                           17.4 ( 28)        23.2 ( 270)                       
                                       yes                                                                          82.0 (132)        76.7 ( 893)                       
                                       <NA>                                                                          0.6 (  1)         0.2 (   2)                       
  Stroke_Dx % (freq)                   Missing                                                                       0.0 (  0)         0.0 (   0)     NaN         5.5   
                                       No stroke diagnosed                                                          80.1 (129)        75.5 ( 880)                       
                                       Stroke diagnosed                                                             13.7 ( 22)        19.1 ( 222)                       
                                       <NA>                                                                          6.2 ( 10)         5.4 (  63)                       
  sympt % (freq)                       missing                                                                       0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       Asymptomatic                                                                100.0 (161)         0.0 (   0)                       
                                       TIA                                                                           0.0 (  0)        46.6 ( 543)                       
                                       minor stroke                                                                  0.0 (  0)        17.2 ( 200)                       
                                       Major stroke                                                                  0.0 (  0)        11.6 ( 135)                       
                                       Amaurosis fugax                                                               0.0 (  0)        16.9 ( 197)                       
                                       Four vessel disease                                                           0.0 (  0)         2.1 (  25)                       
                                       Vertebrobasilary TIA                                                          0.0 (  0)         0.2 (   2)                       
                                       Retinal infarction                                                            0.0 (  0)         1.4 (  16)                       
                                       Symptomatic, but aspecific symtoms                                            0.0 (  0)         3.1 (  36)                       
                                       Contralateral symptomatic occlusion                                           0.0 (  0)         0.5 (   6)                       
                                       retinal infarction                                                            0.0 (  0)         0.3 (   3)                       
                                       armclaudication due to occlusion subclavian artery, CEA needed for bypass     0.0 (  0)         0.1 (   1)                       
                                       retinal infarction + TIAs                                                     0.0 (  0)         0.0 (   0)                       
                                       Ocular ischemic syndrome                                                      0.0 (  0)         0.1 (   1)                       
                                       ischemisch glaucoom                                                           0.0 (  0)         0.0 (   0)                       
                                       subclavian steal syndrome                                                     0.0 (  0)         0.0 (   0)                       
                                       TGA                                                                           0.0 (  0)         0.0 (   0)                       
  Symptoms.5G % (freq)                 Asymptomatic                                                                100.0 (161)         0.0 (   0)    <0.001       0.0   
                                       Ocular                                                                        0.0 (  0)        17.0 ( 198)                       
                                       Other                                                                         0.0 (  0)         5.8 (  68)                       
                                       Retinal infarction                                                            0.0 (  0)         1.6 (  19)                       
                                       Stroke                                                                        0.0 (  0)        28.8 ( 335)                       
                                       TIA                                                                           0.0 (  0)        46.8 ( 545)                       
  AsymptSympt % (freq)                 Asymptomatic                                                                100.0 (161)         0.0 (   0)    <0.001       0.0   
                                       Ocular and others                                                             0.0 (  0)        24.5 ( 285)                       
                                       Symptomatic                                                                   0.0 (  0)        75.5 ( 880)                       
  AsymptSympt2G % (freq)               Asymptomatic                                                                100.0 (161)         0.0 (   0)    <0.001       0.0   
                                       Symptomatic                                                                   0.0 (  0)       100.0 (1165)                       
  restenos % (freq)                    missing                                                                       0.0 (  0)         0.0 (   0)     NaN         2.0   
                                       de novo                                                                      93.2 (150)        95.0 (1107)                       
                                       restenosis                                                                    3.7 (  6)         3.1 (  36)                       
                                       stenose bij angioseal na PTCA                                                 0.0 (  0)         0.0 (   0)                       
                                       <NA>                                                                          3.1 (  5)         1.9 (  22)                       
  stenose % (freq)                     missing                                                                       0.0 (  0)         0.0 (   0)     NaN         2.9   
                                       0-49%                                                                         0.0 (  0)         0.6 (   7)                       
                                       50-70%                                                                        2.5 (  4)         6.2 (  72)                       
                                       70-90%                                                                       50.9 ( 82)        44.6 ( 520)                       
                                       90-99%                                                                       42.9 ( 69)        43.3 ( 504)                       
                                       100% (Occlusion)                                                              0.0 (  0)         0.9 (  11)                       
                                       NA                                                                            0.0 (  0)         0.0 (   0)                       
                                       50-99%                                                                        0.6 (  1)         0.3 (   4)                       
                                       70-99%                                                                        0.0 (  0)         1.2 (  14)                       
                                       99                                                                            0.0 (  0)         0.0 (   0)                       
                                       <NA>                                                                          3.1 (  5)         2.8 (  33)                       
  MedHx_CVD % (freq)                   No                                                                           37.3 ( 60)        36.7 ( 428)     0.965       0.0   
                                       yes                                                                          62.7 (101)        63.3 ( 737)                       
  CAD_history % (freq)                 Missing                                                                       0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       No history CAD                                                               59.0 ( 95)        69.1 ( 805)                       
                                       History CAD                                                                  41.0 ( 66)        30.9 ( 360)                       
  PAOD % (freq)                        missing/no data                                                               0.0 (  0)         0.0 (   0)     NaN         0.0   
                                       no                                                                           73.9 (119)        79.8 ( 930)                       
                                       yes                                                                          26.1 ( 42)        20.2 ( 235)                       
  Peripheral.interv % (freq)           no                                                                           72.7 (117)        83.0 ( 967)     0.004       0.2   
                                       yes                                                                          27.3 ( 44)        16.7 ( 195)                       
                                       <NA>                                                                          0.0 (  0)         0.3 (   3)                       
  EP_composite % (freq)                No data available.                                                            0.0 (  0)         0.0 (   0)     NaN         0.8   
                                       No composite endpoints                                                       68.3 (110)        73.8 ( 860)                       
                                       Composite endpoints                                                          31.7 ( 51)        25.2 ( 294)                       
                                       <NA>                                                                          0.0 (  0)         0.9 (  11)                       
  EP_composite_time (mean (SD))                                                                                    2.579 (0.961)     2.611 (1.130)    0.735       1.0   
  macmean0 (mean (SD))                                                                                             0.802 (1.072)     0.822 (1.275)    0.856       2.2   
  smcmean0 (mean (SD))                                                                                             2.445 (2.594)     1.923 (2.234)    0.007       2.5   
  Macrophages.bin % (freq)             no/minor                                                                     50.3 ( 81)        45.8 ( 533)     0.309       1.8   
                                       moderate/heavy                                                               49.1 ( 79)        52.3 ( 609)                       
                                       <NA>                                                                          0.6 (  1)         2.0 (  23)                       
  SMC.bin % (freq)                     no/minor                                                                     21.7 ( 35)        32.5 ( 379)     0.017       1.7   
                                       moderate/heavy                                                               77.0 (124)        65.8 ( 766)                       
                                       <NA>                                                                          1.2 (  2)         1.7 (  20)                       
  neutrophils (mean (SD))                                                                                        133.447 (437.032) 158.140 (448.512)  0.754      80.9   
  Mast_cells_plaque (mean (SD))                                                                                  123.389 (135.924) 173.244 (168.601)  0.097      83.7   
  IPH.bin % (freq)                     no                                                                           39.1 ( 63)        36.3 ( 423)     0.512       1.5   
                                       yes                                                                          60.2 ( 97)        62.1 ( 723)                       
                                       <NA>                                                                          0.6 (  1)         1.6 (  19)                       
  vessel_density_averaged (mean (SD))                                                                              8.837 (6.727)     8.439 (6.394)    0.480       8.0   
 [ reached getOption("max.print") -- omitted 21 rows ]

CEA patients with plasma MCP1 levels

Showing the baseline table of the CEA patients in the Athero-Express Biobank with plasma MCP1 levels.

NOT AVAILABLE YET

AEDB.CEA.subset.serum <- subset(AEDB.CEA, !is.na(MCP1_plasma))

AEDB.CEA.subset.serum.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.serum, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]

CEA patients with plasma and plaque MCP1 levels

Showing the baseline table of the CEA patients in the Athero-Express Biobank with both plasma and plaque MCP1 levels.

NOT AVAILABLE YET

AEDB.CEA.subset.both <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) & !is.na(MCP1))

AEDB.CEA.subset.both.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.both, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]

Writing the baseline table to Excel format.

# Write basetable
require(openxlsx)
Loading required package: openxlsx
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
           AEDB.CEA.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.AsymptSympt.xlsx"),
           AEDB.CEA.subset.AsymptSympt.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline_Sympt")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
           AEDB.CEA.subset.combo.tableOne,
           row.names = TRUE,
           col.names = TRUE,
           sheetName = "subsetAEDB_Baseline")

# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEAserum.AsymptSympt.xlsx"),
#            AEDB.CEA.subset.serum.tableOne, 
#            row.names = TRUE, 
#            col.names = TRUE, 
#            sheetName = "subsetAEDB_Baseline_serum_Sympt")

Data exploration

Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, natural log transformed + the smallest measurement, and inverse-normal transformation.

MCP1 plaque levels

We will explore the plaque levels. As noted above, we will use MCP1_pg_ug_2015.


summary(AEDB.CEA$MCP1_pg_ug_2015)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0005  0.1373  0.3401  0.6121  0.7232 10.8540    1225 
do.call(rbind , by(AEDB.CEA$MCP1_pg_ug_2015, AEDB.CEA$AsymptSympt2G, summary))
                     Min.    1st Qu.    Median      Mean   3rd Qu.      Max. NA's
Asymptomatic 0.0061401090 0.09779678 0.2148984 0.4950529 0.4982360  5.761795  139
Symptomatic  0.0004584575 0.14408036 0.3510514 0.6264834 0.7412862 10.853968 1086
library(patchwork)

Attaching package: ‘patchwork’

The following object is masked from ‘package:MASS’:

    area
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.
min_MCP1_pg_ug_2015 <- min(AEDB.CEA$MCP1_pg_ug_2015, na.rm = TRUE)
min_MCP1_pg_ug_2015
[1] 0.0004584575
AEDB.CEA$MCP1_pg_ug_2015_LN <- log(AEDB.CEA$MCP1_pg_ug_2015 + min_MCP1_pg_ug_2015)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    # title = "MCP1 plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$MCP1_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ug_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/ug",
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.
p1 

p2 

p3

# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)

MCP1 serum levels

NOT AVAILABLE YET


summary(AEDB.CEA$MCP1)

do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
do.call(rbind , by(AEDB.CEA.subset.serum$MCP1_pg_ug_2015, AEDB.CEA.subset.serum$AsymptSympt2G, summary))
do.call(rbind , by(AEDB.CEA.subset.serum$MCP1, AEDB.CEA.subset.serum$AsymptSympt2G, summary))
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())

min_MCP1 <- min(AEDB.CEA$MCP1, na.rm = TRUE)
min_MCP1

AEDB.CEA$MCP1_LN <- log(AEDB.CEA$MCP1 + min_MCP1)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 serum levels",
                    xlab = "inverse-normal transformation pg/ug",
                    ggtheme = theme_minimal())

p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)

Preliminary conclusion data exploration

In line with the previous work by Marios Georgakis we will apply natural log transformation on all proteins and focus the analysis on MCP1 in serum and plaque.

Analyses

The analyses are focused on three elements:

  1. plaque vulnerability phenotypes
  2. clinical status at inclusion (symptoms)
  3. secondary clinical outcome during three (3) years of follow-up

Covariates & other variables

  1. Age (continuous in 1-year increment). [Age]
  2. Sex (male vs. female). [Gender]
  3. Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
  4. Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [DiabetesStatus]
  5. Smoking (current, ex-, never). [SmokerStatus]
  6. LDL-C levels (continuous). [LDL_final]
  7. Use of lipid-lowering drugs. [Med.Statin.LLD]
  8. Use of antiplatelet drugs. [Med.all.antiplatelet]
  9. eGFR (continuous). [GFR_MDRD]
  10. BMI (continuous). [BMI]
  11. History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combination of [CAD_history, Stroke_history, Peripheral.interv]
  12. Level of stenosis (50-70% vs. 70-99%). [stenose]

Models

We will analyze the data through four different models

  • Model 1: adjusted for age and sex
  • Model 2: adjusted for age, sex, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)

A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque and serum MCP1, IL6, and IL6R levels we will focus on the following plaque vulnerability phenotypes:

  • Percentage of macrophages (continuous trait)
  • Percentage of SMCs (continuous trait)
  • Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
  • Presence of moderate/heavy calcifications (binary trait)
  • Presence of moderate/heavy collagen content (binary trait)
  • Presence of lipid core no/<10% vs. >10% (binary trait)
  • Presence of intraplaque hemorrhage (binary trait)

Continous traits


# macrophages
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$macmean0)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0733  0.3133  0.7676  1.0000 15.1000     720 
min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

Minimum value % macrophages: 0.
AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)

ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

# smooth muscle cells
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$macmean0)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0733  0.3133  0.7676  1.0000 15.1000     720 
min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

Minimum value % smooth muscle cells: 0.
AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)

ggpubr::gghistogram(AEDB.CEA, "SMC_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

# vessel density
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$vessel_density_averaged)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   4.000   7.000   8.318  11.300  48.000     850 
min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity
[1] 0
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

Minimum value number of intraplaque neovessels per 3-4 hotspots: 0.
AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)

ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                    xlab = "natural log-transformed number", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

Binary traits


# calcification
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$Calc.bin)
      no/minor moderate/heavy           NA's 
          1006            849            566 
contrasts(AEDB.CEA$Calc.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CalcificationPlaque)) %>%
  group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())

rm(df)

# collagen
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$Collagen.bin)
      no/minor moderate/heavy           NA's 
           382           1467            572 
contrasts(AEDB.CEA$Collagen.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CollagenPlaque)) %>%
  group_by(Gender, CollagenPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())

rm(df)

# fat 10%
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$Fat.bin_10)
 <10%  >10%  NA's 
  542  1314   565 
contrasts(AEDB.CEA$Fat.bin_10)
       >10%
 <10%     0
 >10%     1
AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)

df <- AEDB.CEA %>%
  filter(!is.na(Fat10Perc)) %>%
  group_by(Gender, Fat10Perc) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())

rm(df)

# IPH
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$IPH.bin)
  no  yes NA's 
 744 1108  569 
contrasts(AEDB.CEA$IPH.bin)
    yes
no    0
yes   1
AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)

df <- AEDB.CEA %>%
  filter(!is.na(IPH)) %>%
  group_by(Gender, IPH) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())

rm(df)

# Symptoms
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$AsymptSympt)
     Asymptomatic Ocular and others       Symptomatic 
              270               540              1611 
contrasts(AEDB.CEA$AsymptSympt)
                  Ocular and others Symptomatic
Asymptomatic                      0           0
Ocular and others                 1           0
Symptomatic                       0           1
AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)

df <- AEDB.CEA %>%
  filter(!is.na(AsymptSympt)) %>%
  group_by(Gender, AsymptSympt) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())

rm(df)

In this section we make some variables to assist with analysis.

AEDB.CEA.samplesize = nrow(AEDB.CEA)
# TRAITS.PROTEIN = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN")
# TRAITS.PROTEIN.RANK = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank")
# TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank", "MCP1_rank")
TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank")

# TRAITS.CON = c("Macrophages_LN", "SMC_LN", "VesselDensity_LN") 
TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "VesselDensity_rank")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH")

# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.   BMI (continuous). [BMI]
# 11.   History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12.   Level of stenosis (50-70% vs. 70-99%). [stenose]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,

COVARIATES_M1 = c("Age", "Gender")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", 
               "SmokerStatus", 
               # "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               # "CAD_history", "Stroke_history", "Peripheral.interv", 
               "MedHx_CVD",
               "stenose")

# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

# COVARIATES_M5 = c(COVARIATES_M2, "IL6_pg_ug_2015_LN")
# COVARIATES_M5rank = c(COVARIATES_M2, "IL6_pg_ug_2015_rank")

Model 1

In this model we correct for Age and Gender.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of serum/plaque MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of MCP1_pg_ug_2015_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale  
          -0.08694            -0.04450             0.10580  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4082 -0.6842  0.0046  0.6589  3.3363 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -0.0417401  0.2249607  -0.186   0.8528  
currentDF[, TRAIT] -0.0450829  0.0294144  -1.533   0.1256  
Age                -0.0006612  0.0031991  -0.207   0.8363  
Gendermale          0.1060371  0.0635635   1.668   0.0955 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9995 on 1164 degrees of freedom
Multiple R-squared:  0.004088,  Adjusted R-squared:  0.001522 
F-statistic: 1.593 on 3 and 1164 DF,  p-value: 0.1894

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: -0.045083 
Standard error............: 0.029414 
Odds ratio (effect size)..: 0.956 
Lower 95% CI..............: 0.902 
Upper 95% CI..............: 1.013 
T-value...................: -1.532681 
P-value...................: 0.1256263 
R^2.......................: 0.004088 
Adjusted r^2..............: 0.001522 
Sample size of AE DB......: 2421 
Sample size of model......: 1168 
Missing data %............: 51.75547 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
          -0.01596            -0.11159  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4354 -0.6542 -0.0098  0.6350  3.3670 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.112040   0.228792   0.490 0.624438    
currentDF[, TRAIT] -0.111735   0.030680  -3.642 0.000283 ***
Age                -0.002497   0.003236  -0.772 0.440423    
Gendermale          0.062484   0.063873   0.978 0.328152    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9952 on 1160 degrees of freedom
Multiple R-squared:  0.01319,   Adjusted R-squared:  0.01064 
F-statistic: 5.169 on 3 and 1160 DF,  p-value: 0.001498

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.111735 
Standard error............: 0.03068 
Odds ratio (effect size)..: 0.894 
Lower 95% CI..............: 0.842 
Upper 95% CI..............: 0.95 
T-value...................: -3.641935 
P-value...................: 0.000282537 
R^2.......................: 0.013191 
Adjusted r^2..............: 0.010639 
Sample size of AE DB......: 2421 
Sample size of model......: 1164 
Missing data %............: 51.92069 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale  
           -0.1146             -0.1291              0.1266  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2793 -0.6743  0.0015  0.6370  3.4081 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        -0.142513   0.233373  -0.611   0.5415    
currentDF[, TRAIT] -0.129023   0.030547  -4.224  2.6e-05 ***
Age                 0.000407   0.003312   0.123   0.9022    
Gendermale          0.126560   0.065772   1.924   0.0546 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.002 on 1088 degrees of freedom
Multiple R-squared:  0.01916,   Adjusted R-squared:  0.01645 
F-statistic: 7.084 on 3 and 1088 DF,  p-value: 0.0001025

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.129023 
Standard error............: 0.030547 
Odds ratio (effect size)..: 0.879 
Lower 95% CI..............: 0.828 
Upper 95% CI..............: 0.933 
T-value...................: -4.223732 
P-value...................: 2.60385e-05 
R^2.......................: 0.019159 
Adjusted r^2..............: 0.016454 
Sample size of AE DB......: 2421 
Sample size of model......: 1092 
Missing data %............: 54.89467 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Binary plaque traits

Analysis of binary plaque traits as a function of serum/plaque MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of MCP1_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
            -0.88365              -0.44949               0.01414              -0.19877  

Degrees of Freedom: 1177 Total (i.e. Null);  1174 Residual
Null Deviance:      1632 
Residual Deviance: 1570     AIC: 1578

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7790  -1.1167  -0.7616   1.1294   1.9050  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.883647   0.460710  -1.918   0.0551 .  
currentDF[, PROTEIN] -0.449493   0.062887  -7.148 8.83e-13 ***
Age                   0.014139   0.006548   2.159   0.0308 *  
Gendermale           -0.198771   0.129909  -1.530   0.1260    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1632.5  on 1177  degrees of freedom
Residual deviance: 1570.1  on 1174  degrees of freedom
AIC: 1578.1

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.449493 
Standard error............: 0.062887 
Odds ratio (effect size)..: 0.638 
Lower 95% CI..............: 0.564 
Upper 95% CI..............: 0.722 
Z-value...................: -7.147681 
P-value...................: 8.825609e-13 
Hosmer and Lemeshow r^2...: 0.038193 
Cox and Snell r^2.........: 0.051552 
Nagelkerke's pseudo r^2...: 0.068747 
Sample size of AE DB......: 2421 
Sample size of model......: 1178 
Missing data %............: 51.34242 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN], 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]  
              1.3310               -0.2324  

Degrees of Freedom: 1178 Total (i.e. Null);  1177 Residual
Null Deviance:      1216 
Residual Deviance: 1205     AIC: 1209

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1020   0.5729   0.6568   0.7158   0.9553  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)           1.143689   0.546619   2.092  0.03641 * 
currentDF[, PROTEIN] -0.231697   0.072337  -3.203  0.00136 **
Age                   0.002892   0.007791   0.371  0.71047   
Gendermale           -0.015641   0.156179  -0.100  0.92023   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1215.6  on 1178  degrees of freedom
Residual deviance: 1205.0  on 1175  degrees of freedom
AIC: 1213

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.231697 
Standard error............: 0.072337 
Odds ratio (effect size)..: 0.793 
Lower 95% CI..............: 0.688 
Upper 95% CI..............: 0.914 
Z-value...................: -3.202999 
P-value...................: 0.001360044 
Hosmer and Lemeshow r^2...: 0.008726 
Cox and Snell r^2.........: 0.008956 
Nagelkerke's pseudo r^2...: 0.013921 
Sample size of AE DB......: 2421 
Sample size of model......: 1179 
Missing data %............: 51.30112 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
            -0.32933               0.15569               0.01093               0.85630  

Degrees of Freedom: 1178 Total (i.e. Null);  1175 Residual
Null Deviance:      1386 
Residual Deviance: 1339     AIC: 1347

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9399  -1.2794   0.6824   0.7695   1.2262  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.329327   0.501681  -0.656   0.5115    
currentDF[, PROTEIN]  0.155685   0.066807   2.330   0.0198 *  
Age                   0.010928   0.007185   1.521   0.1283    
Gendermale            0.856297   0.137117   6.245 4.24e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1386.5  on 1178  degrees of freedom
Residual deviance: 1338.8  on 1175  degrees of freedom
AIC: 1346.8

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.155685 
Standard error............: 0.066807 
Odds ratio (effect size)..: 1.168 
Lower 95% CI..............: 1.025 
Upper 95% CI..............: 1.332 
Z-value...................: 2.330372 
P-value...................: 0.01978647 
Hosmer and Lemeshow r^2...: 0.034376 
Cox and Snell r^2.........: 0.039619 
Nagelkerke's pseudo r^2...: 0.057296 
Sample size of AE DB......: 2421 
Sample size of model......: 1179 
Missing data %............: 51.30112 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale  
             0.02945              -0.12895               0.61756  

Degrees of Freedom: 1175 Total (i.e. Null);  1173 Residual
Null Deviance:      1572 
Residual Deviance: 1546     AIC: 1552

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.650  -1.271   0.884   0.956   1.342  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.157708   0.462022  -0.341   0.7328    
currentDF[, PROTEIN] -0.128693   0.060765  -2.118   0.0342 *  
Age                   0.002737   0.006577   0.416   0.6773    
Gendermale            0.616805   0.129037   4.780 1.75e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1572.3  on 1175  degrees of freedom
Residual deviance: 1545.6  on 1172  degrees of freedom
AIC: 1553.6

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: -0.128693 
Standard error............: 0.060765 
Odds ratio (effect size)..: 0.879 
Lower 95% CI..............: 0.781 
Upper 95% CI..............: 0.99 
Z-value...................: -2.117883 
P-value...................: 0.03418496 
Hosmer and Lemeshow r^2...: 0.016998 
Cox and Snell r^2.........: 0.022471 
Nagelkerke's pseudo r^2...: 0.030474 
Sample size of AE DB......: 2421 
Sample size of model......: 1176 
Missing data %............: 51.42503 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 2

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of serum/plaque MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of MCP1_pg_ug_2015_rank.

- processing Macrophages_rank
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(COVARIATES_M2)` instead of `COVARIATES_M2` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite + 
    Med.Statin.LLD, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]  Hypertension.compositeyes          Med.Statin.LLDyes  
                  0.30069                   -0.05373                   -0.20206                   -0.14604  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3053 -0.6548 -0.0242  0.6293  3.3436 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)                0.2390578  0.6042876   0.396   0.6925  
currentDF[, TRAIT]        -0.0612856  0.0317785  -1.929   0.0541 .
Age                       -0.0022303  0.0039334  -0.567   0.5708  
Gendermale                 0.0974448  0.0709000   1.374   0.1696  
Hypertension.compositeyes -0.2024463  0.0966092  -2.096   0.0364 *
DiabetesStatusDiabetes    -0.0289768  0.0772772  -0.375   0.7078  
SmokerStatusEx-smoker     -0.0230909  0.0731098  -0.316   0.7522  
SmokerStatusNever smoked   0.1337644  0.1030877   1.298   0.1947  
Med.Statin.LLDyes         -0.1502085  0.0774112  -1.940   0.0526 .
Med.all.antiplateletyes    0.0018191  0.1086828   0.017   0.9866  
GFR_MDRD                  -0.0005437  0.0016784  -0.324   0.7461  
BMI                       -0.0026857  0.0088250  -0.304   0.7609  
MedHx_CVDyes               0.0199269  0.0664034   0.300   0.7642  
stenose50-70%              0.2704089  0.4317030   0.626   0.5312  
stenose70-90%              0.3081981  0.4140028   0.744   0.4568  
stenose90-99%              0.1802418  0.4140082   0.435   0.6634  
stenose100% (Occlusion)   -0.2457719  0.5317403  -0.462   0.6440  
stenose50-99%              0.4778476  0.6484366   0.737   0.4613  
stenose70-99%              0.4432860  0.5811508   0.763   0.4458  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.002 on 1000 degrees of freedom
Multiple R-squared:  0.0227,    Adjusted R-squared:  0.005108 
F-statistic:  1.29 on 18 and 1000 DF,  p-value: 0.1851

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: -0.061286 
Standard error............: 0.031778 
Odds ratio (effect size)..: 0.941 
Lower 95% CI..............: 0.884 
Upper 95% CI..............: 1.001 
T-value...................: -1.928524 
P-value...................: 0.05407278 
R^2.......................: 0.0227 
Adjusted r^2..............: 0.005108 
Sample size of AE DB......: 2421 
Sample size of model......: 1019 
Missing data %............: 57.90995 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite + 
    Med.Statin.LLD, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]  Hypertension.compositeyes          Med.Statin.LLDyes  
                   0.3084                    -0.1064                    -0.2034                    -0.1530  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3130 -0.6745 -0.0099  0.6299  3.3696 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)                0.3219731  0.6046171   0.533  0.59448   
currentDF[, TRAIT]        -0.1029968  0.0329730  -3.124  0.00184 **
Age                       -0.0034454  0.0039631  -0.869  0.38485   
Gendermale                 0.0487194  0.0717747   0.679  0.49743   
Hypertension.compositeyes -0.1954166  0.0965630  -2.024  0.04327 * 
DiabetesStatusDiabetes    -0.0219028  0.0772121  -0.284  0.77672   
SmokerStatusEx-smoker     -0.0209122  0.0731694  -0.286  0.77509   
SmokerStatusNever smoked   0.1054769  0.1030387   1.024  0.30624   
Med.Statin.LLDyes         -0.1609021  0.0774630  -2.077  0.03804 * 
Med.all.antiplateletyes   -0.0039324  0.1085928  -0.036  0.97112   
GFR_MDRD                  -0.0004018  0.0016792  -0.239  0.81092   
BMI                       -0.0026011  0.0088289  -0.295  0.76836   
MedHx_CVDyes               0.0116355  0.0664377   0.175  0.86101   
stenose50-70%              0.3063875  0.4313296   0.710  0.47766   
stenose70-90%              0.3397682  0.4136695   0.821  0.41164   
stenose90-99%              0.2223931  0.4138010   0.537  0.59108   
stenose100% (Occlusion)   -0.1771374  0.5313862  -0.333  0.73894   
stenose50-99%              0.5447894  0.6479149   0.841  0.40064   
stenose70-99%              0.4871557  0.5806444   0.839  0.40168   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.001 on 996 degrees of freedom
Multiple R-squared:  0.02833,   Adjusted R-squared:  0.01077 
F-statistic: 1.613 on 18 and 996 DF,  p-value: 0.05023

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.102997 
Standard error............: 0.032973 
Odds ratio (effect size)..: 0.902 
Lower 95% CI..............: 0.846 
Upper 95% CI..............: 0.962 
T-value...................: -3.123675 
P-value...................: 0.00183768 
R^2.......................: 0.028328 
Adjusted r^2..............: 0.010768 
Sample size of AE DB......: 2421 
Sample size of model......: 1015 
Missing data %............: 58.07518 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite + 
    Med.Statin.LLD, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]  Hypertension.compositeyes          Med.Statin.LLDyes  
                   0.2912                    -0.1439                    -0.1849                    -0.1662  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1614 -0.6484 -0.0233  0.6402  3.4332 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                0.1767459  0.6402229   0.276   0.7826    
currentDF[, TRAIT]        -0.1420732  0.0332558  -4.272 2.13e-05 ***
Age                       -0.0009175  0.0040589  -0.226   0.8212    
Gendermale                 0.1109606  0.0733445   1.513   0.1307    
Hypertension.compositeyes -0.1942298  0.1002766  -1.937   0.0531 .  
DiabetesStatusDiabetes    -0.0328877  0.0819413  -0.401   0.6882    
SmokerStatusEx-smoker     -0.0313111  0.0760495  -0.412   0.6806    
SmokerStatusNever smoked   0.1139310  0.1071394   1.063   0.2879    
Med.Statin.LLDyes         -0.1671740  0.0796915  -2.098   0.0362 *  
Med.all.antiplateletyes    0.0397565  0.1144791   0.347   0.7285    
GFR_MDRD                  -0.0010896  0.0017515  -0.622   0.5340    
BMI                       -0.0016953  0.0091572  -0.185   0.8532    
MedHx_CVDyes               0.0074801  0.0688297   0.109   0.9135    
stenose50-70%              0.1841594  0.4727540   0.390   0.6970    
stenose70-90%              0.2563949  0.4542951   0.564   0.5726    
stenose90-99%              0.1436685  0.4538378   0.317   0.7516    
stenose100% (Occlusion)   -0.2562619  0.5637405  -0.455   0.6495    
stenose50-99%              0.5911304  0.6754523   0.875   0.3817    
stenose70-99%              0.1673943  0.6766921   0.247   0.8047    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.005 on 932 degrees of freedom
Multiple R-squared:  0.03977,   Adjusted R-squared:  0.02122 
F-statistic: 2.144 on 18 and 932 DF,  p-value: 0.003707

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.142073 
Standard error............: 0.033256 
Odds ratio (effect size)..: 0.868 
Lower 95% CI..............: 0.813 
Upper 95% CI..............: 0.926 
T-value...................: -4.272134 
P-value...................: 2.134847e-05 
R^2.......................: 0.039768 
Adjusted r^2..............: 0.021223 
Sample size of AE DB......: 2421 
Sample size of model......: 951 
Missing data %............: 60.71871 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Binary plaque traits

Analysis of binary plaque traits as a function of serum/plaque MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of MCP1_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + SmokerStatus + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)      currentDF[, PROTEIN]                       Age     SmokerStatusEx-smoker  SmokerStatusNever smoked             stenose50-70%             stenose70-90%  
                -1.16173                  -0.47346                   0.01896                  -0.40609                  -0.44306                  -0.56758                  -0.01349  
           stenose90-99%   stenose100% (Occlusion)             stenose50-99%             stenose70-99%  
                 0.18538                   1.15456                 -14.43672                  -0.79787  

Degrees of Freedom: 1023 Total (i.e. Null);  1013 Residual
Null Deviance:      1417 
Residual Deviance: 1335     AIC: 1357

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8447  -1.0810  -0.7025   1.1078   2.0019  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -1.522e+00  1.248e+00  -1.220  0.22262    
currentDF[, PROTEIN]      -4.740e-01  6.875e-02  -6.895 5.38e-12 ***
Age                        1.797e-02  8.224e-03   2.185  0.02892 *  
Gendermale                -9.953e-02  1.469e-01  -0.677  0.49809    
Hypertension.compositeyes  2.389e-01  2.017e-01   1.185  0.23618    
DiabetesStatusDiabetes    -2.266e-01  1.619e-01  -1.400  0.16160    
SmokerStatusEx-smoker     -4.096e-01  1.525e-01  -2.687  0.00722 ** 
SmokerStatusNever smoked  -4.851e-01  2.157e-01  -2.249  0.02454 *  
Med.Statin.LLDyes         -1.636e-01  1.607e-01  -1.019  0.30842    
Med.all.antiplateletyes   -7.738e-02  2.248e-01  -0.344  0.73071    
GFR_MDRD                  -5.302e-04  3.511e-03  -0.151  0.87997    
BMI                        2.166e-02  1.839e-02   1.178  0.23892    
MedHx_CVDyes              -1.660e-02  1.377e-01  -0.121  0.90406    
stenose50-70%             -5.216e-01  8.838e-01  -0.590  0.55510    
stenose70-90%              9.803e-03  8.419e-01   0.012  0.99071    
stenose90-99%              2.028e-01  8.420e-01   0.241  0.80969    
stenose100% (Occlusion)    1.181e+00  1.175e+00   1.005  0.31496    
stenose50-99%             -1.444e+01  4.192e+02  -0.034  0.97252    
stenose70-99%             -7.533e-01  1.208e+00  -0.624  0.53294    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1417.5  on 1023  degrees of freedom
Residual deviance: 1329.0  on 1005  degrees of freedom
AIC: 1367

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.474031 
Standard error............: 0.068749 
Odds ratio (effect size)..: 0.622 
Lower 95% CI..............: 0.544 
Upper 95% CI..............: 0.712 
Z-value...................: -6.895134 
P-value...................: 5.381419e-12 
Hosmer and Lemeshow r^2...: 0.062418 
Cox and Snell r^2.........: 0.082776 
Nagelkerke's pseudo r^2...: 0.110443 
Sample size of AE DB......: 2421 
Sample size of model......: 1024 
Missing data %............: 57.70343 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    SmokerStatus + BMI + MedHx_CVD, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)      currentDF[, PROTEIN]     SmokerStatusEx-smoker  SmokerStatusNever smoked                       BMI              MedHx_CVDyes  
                 0.47638                  -0.24086                  -0.38304                  -0.63224                   0.03905                   0.25051  

Degrees of Freedom: 1024 Total (i.e. Null);  1019 Residual
Null Deviance:      1048 
Residual Deviance: 1025     AIC: 1037

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2698   0.4534   0.6199   0.7192   1.1840  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.385e+01  9.628e+02   0.014  0.98852   
currentDF[, PROTEIN]      -2.330e-01  7.922e-02  -2.941  0.00327 **
Age                        1.362e-02  9.731e-03   1.400  0.16155   
Gendermale                -4.542e-02  1.763e-01  -0.258  0.79672   
Hypertension.compositeyes  2.446e-01  2.286e-01   1.070  0.28467   
DiabetesStatusDiabetes     7.902e-02  1.972e-01   0.401  0.68861   
SmokerStatusEx-smoker     -4.387e-01  1.893e-01  -2.317  0.02051 * 
SmokerStatusNever smoked  -7.570e-01  2.485e-01  -3.046  0.00232 **
Med.Statin.LLDyes          2.795e-03  1.921e-01   0.015  0.98839   
Med.all.antiplateletyes    2.526e-01  2.597e-01   0.973  0.33067   
GFR_MDRD                   5.011e-03  4.226e-03   1.186  0.23576   
BMI                        4.166e-02  2.314e-02   1.801  0.07175 . 
MedHx_CVDyes               2.219e-01  1.635e-01   1.357  0.17468   
stenose50-70%             -1.480e+01  9.628e+02  -0.015  0.98774   
stenose70-90%             -1.509e+01  9.628e+02  -0.016  0.98750   
stenose90-99%             -1.519e+01  9.628e+02  -0.016  0.98741   
stenose100% (Occlusion)    1.169e-01  1.242e+03   0.000  0.99992   
stenose50-99%             -3.410e-02  1.515e+03   0.000  0.99998   
stenose70-99%             -1.464e+01  9.628e+02  -0.015  0.98787   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1047.6  on 1024  degrees of freedom
Residual deviance: 1010.2  on 1006  degrees of freedom
AIC: 1048.2

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.232976 
Standard error............: 0.079216 
Odds ratio (effect size)..: 0.792 
Lower 95% CI..............: 0.678 
Upper 95% CI..............: 0.925 
Z-value...................: -2.941031 
P-value...................: 0.003271218 
Hosmer and Lemeshow r^2...: 0.035759 
Cox and Snell r^2.........: 0.035889 
Nagelkerke's pseudo r^2...: 0.056063 
Sample size of AE DB......: 2421 
Sample size of model......: 1025 
Missing data %............: 57.66212 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + SmokerStatus + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)      currentDF[, PROTEIN]                Gendermale     SmokerStatusEx-smoker  SmokerStatusNever smoked             stenose50-70%             stenose70-90%  
                 13.8072                    0.1608                    0.9217                   -0.2543                    0.3188                  -13.4987                  -13.3997  
           stenose90-99%   stenose100% (Occlusion)             stenose50-99%             stenose70-99%  
                -13.2648                  -13.8075                  -15.8024                  -14.6344  

Degrees of Freedom: 1024 Total (i.e. Null);  1014 Residual
Null Deviance:      1208 
Residual Deviance: 1153     AIC: 1175

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0874  -1.2279   0.6773   0.7958   1.5145  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.323e+01  3.540e+02   0.037   0.9702    
currentDF[, PROTEIN]       1.538e-01  7.309e-02   2.105   0.0353 *  
Age                        7.892e-03  8.954e-03   0.881   0.3781    
Gendermale                 9.325e-01  1.563e-01   5.966 2.43e-09 ***
Hypertension.compositeyes -3.351e-02  2.222e-01  -0.151   0.8802    
DiabetesStatusDiabetes    -1.599e-01  1.759e-01  -0.909   0.3634    
SmokerStatusEx-smoker     -2.887e-01  1.683e-01  -1.716   0.0862 .  
SmokerStatusNever smoked   2.644e-01  2.474e-01   1.069   0.2852    
Med.Statin.LLDyes         -2.292e-01  1.845e-01  -1.242   0.2141    
Med.all.antiplateletyes    1.095e-01  2.495e-01   0.439   0.6608    
GFR_MDRD                   4.634e-04  3.879e-03   0.119   0.9049    
BMI                        4.721e-03  1.983e-02   0.238   0.8118    
MedHx_CVDyes               7.604e-02  1.518e-01   0.501   0.6164    
stenose50-70%             -1.352e+01  3.539e+02  -0.038   0.9695    
stenose70-90%             -1.339e+01  3.539e+02  -0.038   0.9698    
stenose90-99%             -1.327e+01  3.539e+02  -0.037   0.9701    
stenose100% (Occlusion)   -1.382e+01  3.540e+02  -0.039   0.9689    
stenose50-99%             -1.581e+01  3.540e+02  -0.045   0.9644    
stenose70-99%             -1.472e+01  3.540e+02  -0.042   0.9668    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1207.9  on 1024  degrees of freedom
Residual deviance: 1149.4  on 1006  degrees of freedom
AIC: 1187.4

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.153844 
Standard error............: 0.073089 
Odds ratio (effect size)..: 1.166 
Lower 95% CI..............: 1.011 
Upper 95% CI..............: 1.346 
Z-value...................: 2.104886 
P-value...................: 0.03530121 
Hosmer and Lemeshow r^2...: 0.048471 
Cox and Snell r^2.........: 0.055521 
Nagelkerke's pseudo r^2...: 0.080203 
Sample size of AE DB......: 2421 
Sample size of model......: 1025 
Missing data %............: 57.66212 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Med.Statin.LLD + BMI + MedHx_CVD, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale     Med.Statin.LLDyes                   BMI          MedHx_CVDyes  
            -0.71077              -0.14275               0.51657              -0.27539               0.03056               0.36067  

Degrees of Freedom: 1022 Total (i.e. Null);  1017 Residual
Null Deviance:      1367 
Residual Deviance: 1337     AIC: 1349

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9526  -1.2816   0.8168   0.9931   1.5440  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -0.3744407  1.2806515  -0.292   0.7700    
currentDF[, PROTEIN]      -0.1420106  0.0661286  -2.147   0.0318 *  
Age                        0.0002745  0.0081696   0.034   0.9732    
Gendermale                 0.5755452  0.1456998   3.950 7.81e-05 ***
Hypertension.compositeyes -0.1304980  0.2014410  -0.648   0.5171    
DiabetesStatusDiabetes    -0.1239874  0.1610422  -0.770   0.4414    
SmokerStatusEx-smoker     -0.1106836  0.1537985  -0.720   0.4717    
SmokerStatusNever smoked  -0.1474556  0.2125316  -0.694   0.4878    
Med.Statin.LLDyes         -0.2598492  0.1641604  -1.583   0.1134    
Med.all.antiplateletyes    0.0938507  0.2263983   0.415   0.6785    
GFR_MDRD                  -0.0048629  0.0035299  -1.378   0.1683    
BMI                        0.0346557  0.0186016   1.863   0.0625 .  
MedHx_CVDyes               0.3433350  0.1371721   2.503   0.0123 *  
stenose50-70%             -0.3005803  0.9270679  -0.324   0.7458    
stenose70-90%             -0.1234783  0.8929528  -0.138   0.8900    
stenose90-99%              0.1233878  0.8933095   0.138   0.8901    
stenose100% (Occlusion)   -0.3672092  1.1204001  -0.328   0.7431    
stenose50-99%             -0.5109985  1.3494741  -0.379   0.7049    
stenose70-99%              1.3028582  1.4085773   0.925   0.3550    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1367.4  on 1022  degrees of freedom
Residual deviance: 1325.7  on 1004  degrees of freedom
AIC: 1363.7

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: -0.142011 
Standard error............: 0.066129 
Odds ratio (effect size)..: 0.868 
Lower 95% CI..............: 0.762 
Upper 95% CI..............: 0.988 
Z-value...................: -2.14749 
P-value...................: 0.03175426 
Hosmer and Lemeshow r^2...: 0.030475 
Cox and Snell r^2.........: 0.039915 
Nagelkerke's pseudo r^2...: 0.054139 
Sample size of AE DB......: 2421 
Sample size of model......: 1023 
Missing data %............: 57.74473 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque and serum MCP1, IL6, and IL6R levels and the ‘clinical status’ of the plaque in terms of presence of patients’ symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

  • stroke
  • TIA
  • retinal infarction
  • amaurosis fugax
  • asymptomatic

Model 1

In this model we correct for Age, and Gender.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of MCP1_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
             0.24776               0.26397               0.03239              -0.43507  

Degrees of Freedom: 1195 Total (i.e. Null);  1192 Residual
Null Deviance:      826.5 
Residual Deviance: 804.7    AIC: 812.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5654   0.3738   0.4440   0.5167   0.8434  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)   
(Intercept)           0.24776    0.69360   0.357  0.72093   
currentDF[, PROTEIN]  0.26397    0.09376   2.815  0.00487 **
Age                   0.03239    0.01011   3.203  0.00136 **
Gendermale           -0.43507    0.21792  -1.996  0.04588 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 826.52  on 1195  degrees of freedom
Residual deviance: 804.69  on 1192  degrees of freedom
AIC: 812.69

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.263965 
Standard error............: 0.09376 
Odds ratio (effect size)..: 1.302 
Lower 95% CI..............: 1.083 
Upper 95% CI..............: 1.565 
Z-value...................: 2.815333 
P-value...................: 0.004872671 
Hosmer and Lemeshow r^2...: 0.026418 
Cox and Snell r^2.........: 0.018091 
Nagelkerke's pseudo r^2...: 0.036257 
Sample size of AE DB......: 2421 
Sample size of model......: 1196 
Missing data %............: 50.59893 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 2

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                MedHx_CVD + + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of MCP1_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Med.all.antiplatelet + GFR_MDRD + stenose, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age               Gendermale  Med.all.antiplateletyes                 GFR_MDRD            stenose50-70%  
              15.334896                 0.272930                 0.032194                -0.427140                -0.929815                 0.007921               -13.442880  
          stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
             -15.004949               -14.749153                -0.339553               -15.814682                -0.541412  

Degrees of Freedom: 1035 Total (i.e. Null);  1024 Residual
Null Deviance:      726.5 
Residual Deviance: 689.3    AIC: 713.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + +stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0446   0.3116   0.4390   0.5442   0.9328  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.593e+01  9.540e+02   0.017   0.9867   
currentDF[, PROTEIN]       2.558e-01  1.018e-01   2.512   0.0120 * 
Age                        3.508e-02  1.258e-02   2.788   0.0053 **
Gendermale                -3.507e-01  2.366e-01  -1.482   0.1383   
Hypertension.compositeyes -3.437e-01  3.443e-01  -0.998   0.3182   
DiabetesStatusDiabetes    -3.937e-02  2.423e-01  -0.162   0.8709   
SmokerStatusEx-smoker     -3.416e-01  2.335e-01  -1.463   0.1434   
SmokerStatusNever smoked   2.040e-04  3.564e-01   0.001   0.9995   
Med.Statin.LLDyes         -1.797e-01  2.660e-01  -0.676   0.4991   
Med.all.antiplateletyes   -9.093e-01  4.789e-01  -1.899   0.0576 . 
GFR_MDRD                   7.160e-03  5.456e-03   1.312   0.1894   
BMI                       -1.027e-02  2.789e-02  -0.368   0.7128   
MedHx_CVDyes               9.204e-02  2.096e-01   0.439   0.6606   
stenose50-70%             -1.338e+01  9.540e+02  -0.014   0.9888   
stenose70-90%             -1.495e+01  9.540e+02  -0.016   0.9875   
stenose90-99%             -1.472e+01  9.540e+02  -0.015   0.9877   
stenose100% (Occlusion)   -3.915e-01  1.230e+03   0.000   0.9997   
stenose50-99%             -1.592e+01  9.540e+02  -0.017   0.9867   
stenose70-99%             -5.044e-01  1.190e+03   0.000   0.9997   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 726.47  on 1035  degrees of freedom
Residual deviance: 684.24  on 1017  degrees of freedom
AIC: 722.24

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.255797 
Standard error............: 0.10185 
Odds ratio (effect size)..: 1.291 
Lower 95% CI..............: 1.058 
Upper 95% CI..............: 1.577 
Z-value...................: 2.511506 
P-value...................: 0.01202173 
Hosmer and Lemeshow r^2...: 0.058134 
Cox and Snell r^2.........: 0.039945 
Nagelkerke's pseudo r^2...: 0.079253 
Sample size of AE DB......: 2421 
Sample size of model......: 1036 
Missing data %............: 57.20777 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque and serum MCP1, IL6, and IL6R levels and secondary cardiovascular events over a three-year follow-up period.

The primary outcome is defined as “a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death”, i.e. major adverse cardiovascular events (MACE). Variable: epmajor.3years, these include: - myocardial infarction (MI) - cerebral infarction (CVA/stroke) - cardiovascular death (exact cause to be investigated) - cerebral bleeding (CVA/stroke) - fatal myocardial infarction (MI) - fatal cerebral infarction - fatal cerebral bleeding - sudden death - fatal heart failure - fatal aneurysm rupture - other cardiovascular death..

The secondary outcomes will be

  • incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: epstroke.3years, these include:
    • cerebral infarction (CVA/stroke)
    • cerebral bleeding (CVA/stroke)
    • fatal cerebral infarction
    • fatal cerebral bleeding.
  • incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: epcoronary.3years, these include:
    • myocardial infarction (MI)
    • coronary angioplasty (PCI/PTCA)
    • cardiovascular death (exact cause to be investigated)
    • coronary bypass (CABG)
    • fatal myocardial infarction (MI)
    • sudden death.
  • cardiovascular death - variable: epcvdeath.3years, these include:
    • cardiovascular death (exact cause to be investigated)
    • fatal myocardial infarction (MI)
    • fatal cerebral infarction
    • fatal cerebral bleeding
    • sudden death
    • fatal heart failure
    • fatal aneurysm rupture
    • other cardiovascular death..

30- and 90-days FU events

We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up ‘time-to-event’ variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up.

cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90

# Fix maximum FU of 30 and 90 days
AEDB <- AEDB %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)


rm(AEDB.temp)

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)


rm(AEDB.CEA.temp)

Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:

  • MACE (epmajor.3years)
  • Stroke (epstroke.3years)
  • Coronary events (epcoronary.3years)
  • Cardiovascular death (epcvdeath.3years)
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times
AEDB <- AEDB %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

attach(AEDB)
AEDB[,"epmajor.30days"] <- AEDB$epmajor.3years
AEDB$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB[,"epstroke.30days"] <- AEDB$epstroke.3years
AEDB$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB[,"epcoronary.30days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB[,"epcvdeath.30days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB[,"epmajor.90days"] <- AEDB$epmajor.3years
AEDB$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB[,"epstroke.90days"] <- AEDB$epstroke.3years
AEDB$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB[,"epcoronary.90days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB[,"epcvdeath.90days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)


rm(AEDB.temp)

attach(AEDB.CEA)
AEDB.CEA[,"epmajor.30days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epstroke.30days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcoronary.30days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcvdeath.30days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epmajor.90days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epstroke.90days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcoronary.90days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcvdeath.90days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB.CEA)

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)


rm(AEDB.CEA.temp)

Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.

# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
Loading required package: survival
install.packages.auto("survminer")
Loading required package: survminer
install.packages.auto("Hmisc")
Loading required package: Hmisc
Loading required package: lattice
Loading required package: Formula

Attaching package: ‘Hmisc’

The following objects are masked from ‘package:dplyr’:

    src, summarize

The following objects are masked from ‘package:base’:

    format.pval, units
cat("* Creating function to summarize Cox regression and prepare container for results.")
* Creating function to summarize Cox regression and prepare container for results.
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
  cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(protein, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", protein, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
* Check the cases per event type - for sanity.
for (events in endpoints){
  require(labelled)
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}
[1] "Printing the summary of: epmajor.3years"

   0    1 
2033  265 
[1] "Printing the summary of: epstroke.3years"

   0    1 
2169  130 
[1] "Printing the summary of: epcoronary.3years"

   0    1 
2117  182 
[1] "Printing the summary of: epcvdeath.3years"

   0    1 
2208   90 
cat("* Check distribution of events over time - for sanity.")
* Check distribution of events over time - for sanity.
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}
[1] "Printing the summary of: ep_major_t_3years"
 ep_major_t_3years
 Min.   :0.000    
 1st Qu.:2.708    
 Median :3.000    
 Mean   :2.573    
 3rd Qu.:3.000    
 Max.   :3.000    
 NA's   :125      
[1] "Printing the summary of: ep_stroke_t_3years"
 ep_stroke_t_3years
 Min.   :0.000     
 1st Qu.:2.877     
 Median :3.000     
 Mean   :2.623     
 3rd Qu.:3.000     
 Max.   :3.000     
 NA's   :125       
[1] "Printing the summary of: ep_coronary_t_3years"
 ep_coronary_t_3years
 Min.   :0.000       
 1st Qu.:2.783       
 Median :3.000       
 Mean   :2.622       
 3rd Qu.:3.000       
 Max.   :3.000       
 NA's   :125         
[1] "Printing the summary of: ep_cvdeath_t_3years"
 ep_cvdeath_t_3years
 Min.   :0.00274    
 1st Qu.:2.91233    
 Median :3.00000    
 Mean   :2.70878    
 3rd Qu.:3.00000    
 Max.   :3.00000    
 NA's   :125        
for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}
[1] "Printing the distribution of: ep_major_t_3years"
[1] "Printing the distribution of: ep_stroke_t_3years"
[1] "Printing the distribution of: ep_coronary_t_3years"
[1] "Printing the distribution of: ep_cvdeath_t_3years"

times30 = c("ep_major_t_30days", 
          "ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")

endpoints30 = c("epmajor.30days", 
              "epstroke.30days", "epcoronary.30days", "epcvdeath.30days")

cat("* Check the cases per event type - for sanity.")
* Check the cases per event type - for sanity.
for (events in endpoints30){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}
[1] "Printing the summary of: epmajor.30days"

   0    1 
2220   78 
[1] "Printing the summary of: epstroke.30days"

   0    1 
2246   53 
[1] "Printing the summary of: epcoronary.30days"

   0    1 
2265   34 
[1] "Printing the summary of: epcvdeath.30days"

   0    1 
2286   12 
cat("* Check distribution of events over time - for sanity.")
* Check distribution of events over time - for sanity.
for (eventtimes in times30){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}
[1] "Printing the summary of: ep_major_t_30days"
 ep_major_t_30days
 Min.   : 0.00    
 1st Qu.:30.00    
 Median :30.00    
 Mean   :29.09    
 3rd Qu.:30.00    
 Max.   :30.00    
 NA's   :125      
[1] "Printing the summary of: ep_stroke_t_30days"
 ep_stroke_t_30days
 Min.   : 0.00     
 1st Qu.:30.00     
 Median :30.00     
 Mean   :29.32     
 3rd Qu.:30.00     
 Max.   :30.00     
 NA's   :125       
[1] "Printing the summary of: ep_coronary_t_30days"
 ep_coronary_t_30days
 Min.   : 0.00       
 1st Qu.:30.00       
 Median :30.00       
 Mean   :29.54       
 3rd Qu.:30.00       
 Max.   :30.00       
 NA's   :125         
[1] "Printing the summary of: ep_cvdeath_t_30days"
 ep_cvdeath_t_30days
 Min.   : 1.001     
 1st Qu.:30.000     
 Median :30.000     
 Mean   :29.854     
 3rd Qu.:30.000     
 Max.   :30.000     
 NA's   :125        
for (eventtime in times30){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}
[1] "Printing the distribution of: ep_major_t_30days"
[1] "Printing the distribution of: ep_stroke_t_30days"
[1] "Printing the distribution of: ep_coronary_t_30days"
[1] "Printing the distribution of: ep_cvdeath_t_30days"

times90 = c("ep_major_t_90days", 
          "ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")

endpoints90 = c("epmajor.90days", 
              "epstroke.90days", "epcoronary.90days", "epcvdeath.90days")

cat("* Check the cases per event type - for sanity.")
* Check the cases per event type - for sanity.
for (events in endpoints90){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}
[1] "Printing the summary of: epmajor.90days"

   0    1 
2204   94 
[1] "Printing the summary of: epstroke.90days"

   0    1 
2239   60 
[1] "Printing the summary of: epcoronary.90days"

   0    1 
2255   44 
[1] "Printing the summary of: epcvdeath.90days"

   0    1 
2279   19 
cat("* Check distribution of events over time - for sanity.")
* Check distribution of events over time - for sanity.
for (eventtimes in times90){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}
[1] "Printing the summary of: ep_major_t_90days"
 ep_major_t_90days
 Min.   : 0.00    
 1st Qu.:90.00    
 Median :90.00    
 Mean   :86.74    
 3rd Qu.:90.00    
 Max.   :90.00    
 NA's   :125      
[1] "Printing the summary of: ep_stroke_t_90days"
 ep_stroke_t_90days
 Min.   : 0.00     
 1st Qu.:90.00     
 Median :90.00     
 Mean   :87.51     
 3rd Qu.:90.00     
 Max.   :90.00     
 NA's   :125       
[1] "Printing the summary of: ep_coronary_t_90days"
 ep_coronary_t_90days
 Min.   : 0.0        
 1st Qu.:90.0        
 Median :90.0        
 Mean   :88.2        
 3rd Qu.:90.0        
 Max.   :90.0        
 NA's   :125         
[1] "Printing the summary of: ep_cvdeath_t_90days"
 ep_cvdeath_t_90days
 Min.   : 1.001     
 1st Qu.:90.000     
 Median :90.000     
 Mean   :89.320     
 3rd Qu.:90.000     
 Max.   :90.000     
 NA's   :125        
for (eventtime in times90){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}
[1] "Printing the distribution of: ep_major_t_90days"
[1] "Printing the distribution of: ep_stroke_t_90days"
[1] "Printing the distribution of: ep_coronary_t_90days"
[1] "Printing the distribution of: ep_cvdeath_t_90days"

Cox regressions

Let’s perform the actual Cox-regressions. We will apply a couple of models:

  • Model 1: adjusted for age and sex
  • Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis

3 years follow-up

MODEL 1

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}
* Analyzing the effect of plaque proteins on [epmajor.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34055,0.00105) [ 0.00105,3.34055] 
               598                598 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1184, number of events= 139 
   (1237 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)     z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 0.037093  1.037789 0.169988 0.218 0.827268    
Age                                                       0.033469  1.034035 0.009876 3.389 0.000702 ***
Gendermale                                                0.336307  1.399769 0.199676 1.684 0.092131 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055]     1.038     0.9636    0.7437     1.448
Age                                                           1.034     0.9671    1.0142     1.054
Gendermale                                                    1.400     0.7144    0.9464     2.070

Concordance= 0.588  (se = 0.025 )
Likelihood ratio test= 15.11  on 3 df,   p=0.002
Wald test            = 14.36  on 3 df,   p=0.002
Score (logrank) test = 14.43  on 3 df,   p=0.002


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.037093 
Standard error............: 0.169988 
Odds ratio (effect size)..: 1.038 
Lower 95% CI..............: 0.744 
Upper 95% CI..............: 1.448 
T-value...................: 0.218206 
P-value...................: 0.8272683 
Sample size in model......: 1184 
Number of events..........: 139 
* Analyzing the effect of plaque proteins on [epstroke.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34055,0.00105) [ 0.00105,3.34055] 
               598                598 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1184, number of events= 73 
   (1237 observations deleted due to missingness)

                                                             coef exp(coef) se(coef)     z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 0.13646   1.14621  0.23508 0.580   0.5616  
Age                                                       0.03440   1.03500  0.01356 2.537   0.0112 *
Gendermale                                                0.06281   1.06483  0.25901 0.243   0.8084  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055]     1.146     0.8724    0.7230     1.817
Age                                                           1.035     0.9662    1.0079     1.063
Gendermale                                                    1.065     0.9391    0.6409     1.769

Concordance= 0.597  (se = 0.033 )
Likelihood ratio test= 7.12  on 3 df,   p=0.07
Wald test            = 6.89  on 3 df,   p=0.08
Score (logrank) test = 6.92  on 3 df,   p=0.07


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.136457 
Standard error............: 0.235075 
Odds ratio (effect size)..: 1.146 
Lower 95% CI..............: 0.723 
Upper 95% CI..............: 1.817 
T-value...................: 0.580483 
P-value...................: 0.5615888 
Sample size in model......: 1184 
Number of events..........: 73 
* Analyzing the effect of plaque proteins on [epcoronary.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34055,0.00105) [ 0.00105,3.34055] 
               598                598 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1184, number of events= 91 
   (1237 observations deleted due to missingness)

                                                               coef exp(coef)  se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] -0.174454  0.839916  0.210481 -0.829   0.4072  
Age                                                        0.007135  1.007160  0.011848  0.602   0.5471  
Gendermale                                                 0.666235  1.946893  0.269214  2.475   0.0133 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055]    0.8399     1.1906     0.556     1.269
Age                                                          1.0072     0.9929     0.984     1.031
Gendermale                                                   1.9469     0.5136     1.149     3.300

Concordance= 0.577  (se = 0.03 )
Likelihood ratio test= 7.93  on 3 df,   p=0.05
Wald test            = 7.07  on 3 df,   p=0.07
Score (logrank) test = 7.29  on 3 df,   p=0.06


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: -0.174454 
Standard error............: 0.210481 
Odds ratio (effect size)..: 0.84 
Lower 95% CI..............: 0.556 
Upper 95% CI..............: 1.269 
T-value...................: -0.828833 
P-value...................: 0.4071991 
Sample size in model......: 1184 
Number of events..........: 91 
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34055,0.00105) [ 0.00105,3.34055] 
               598                598 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1184, number of events= 45 
   (1237 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] -0.11090   0.89502  0.29877 -0.371   0.7105    
Age                                                        0.08444   1.08811  0.01930  4.375 1.21e-05 ***
Gendermale                                                 0.89483   2.44691  0.41200  2.172   0.0299 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055]     0.895     1.1173    0.4983     1.608
Age                                                           1.088     0.9190    1.0477     1.130
Gendermale                                                    2.447     0.4087    1.0913     5.487

Concordance= 0.71  (se = 0.036 )
Likelihood ratio test= 26.84  on 3 df,   p=6e-06
Wald test            = 23.16  on 3 df,   p=4e-05
Score (logrank) test = 23.81  on 3 df,   p=3e-05


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: -0.110904 
Standard error............: 0.298774 
Odds ratio (effect size)..: 0.895 
Lower 95% CI..............: 0.498 
Upper 95% CI..............: 1.608 
T-value...................: -0.371199 
P-value...................: 0.7104893 
Sample size in model......: 1184 
Number of events..........: 45 

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

MODEL 2

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}
* Analyzing the effect of plaque proteins on [epmajor.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34055,0.00105) [ 0.00105,3.34055] 
               598                598 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 1027, number of events= 115 
   (1394 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055]  1.327e-01  1.142e+00  1.898e-01  0.699   0.4843    
Age                                                        3.278e-02  1.033e+00  1.275e-02  2.572   0.0101 *  
Gendermale                                                 3.790e-01  1.461e+00  2.280e-01  1.662   0.0964 .  
Hypertension.compositeno                                  -4.193e-01  6.575e-01  3.564e-01 -1.177   0.2393    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -1.633e-02  9.838e-01  2.234e-01 -0.073   0.9417    
SmokerStatusEx-smoker                                     -5.028e-01  6.049e-01  2.095e-01 -2.400   0.0164 *  
SmokerStatusNever smoked                                  -8.142e-01  4.430e-01  3.415e-01 -2.385   0.0171 *  
Med.Statin.LLDno                                           2.579e-01  1.294e+00  2.155e-01  1.197   0.2314    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     4.283e-01  1.535e+00  2.635e-01  1.626   0.1040    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -1.969e-02  9.805e-01  4.925e-03 -3.997 6.42e-05 ***
BMI                                                        5.402e-02  1.056e+00  2.613e-02  2.067   0.0387 *  
MedHx_CVDyes                                               5.306e-01  1.700e+00  2.220e-01  2.391   0.0168 *  
stenose0-49%                                              -1.560e+01  1.677e-07  2.462e+03 -0.006   0.9949    
stenose50-70%                                             -8.183e-01  4.412e-01  8.701e-01 -0.941   0.3470    
stenose70-90%                                             -2.441e-01  7.834e-01  7.298e-01 -0.335   0.7380    
stenose90-99%                                             -2.079e-01  8.123e-01  7.282e-01 -0.285   0.7753    
stenose100% (Occlusion)                                   -4.754e-02  9.536e-01  1.244e+00 -0.038   0.9695    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.529e+01  2.289e-07  2.920e+03 -0.005   0.9958    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 1.142e+00  8.757e-01   0.78722    1.6565
Age                                                       1.033e+00  9.678e-01   1.00783    1.0595
Gendermale                                                1.461e+00  6.845e-01   0.93440    2.2840
Hypertension.compositeno                                  6.575e-01  1.521e+00   0.32699    1.3220
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.838e-01  1.016e+00   0.63498    1.5242
SmokerStatusEx-smoker                                     6.049e-01  1.653e+00   0.40120    0.9119
SmokerStatusNever smoked                                  4.430e-01  2.257e+00   0.22685    0.8650
Med.Statin.LLDno                                          1.294e+00  7.727e-01   0.84830    1.9746
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.535e+00  6.516e-01   0.91566    2.5720
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.805e-01  1.020e+00   0.97109    0.9900
BMI                                                       1.056e+00  9.474e-01   1.00281    1.1110
MedHx_CVDyes                                              1.700e+00  5.882e-01   1.10031    2.6266
stenose0-49%                                              1.677e-07  5.962e+06   0.00000       Inf
stenose50-70%                                             4.412e-01  2.267e+00   0.08017    2.4278
stenose70-90%                                             7.834e-01  1.276e+00   0.18741    3.2747
stenose90-99%                                             8.123e-01  1.231e+00   0.19494    3.3848
stenose100% (Occlusion)                                   9.536e-01  1.049e+00   0.08333   10.9119
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.289e-07  4.369e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.697  (se = 0.023 )
Likelihood ratio test= 63.79  on 18 df,   p=5e-07
Wald test            = 58.83  on 18 df,   p=3e-06
Score (logrank) test = 62.19  on 18 df,   p=9e-07


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.132746 
Standard error............: 0.189795 
Odds ratio (effect size)..: 1.142 
Lower 95% CI..............: 0.787 
Upper 95% CI..............: 1.657 
T-value...................: 0.699416 
P-value...................: 0.4842918 
Sample size in model......: 1027 
Number of events..........: 115 
* Analyzing the effect of plaque proteins on [epstroke.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34055,0.00105) [ 0.00105,3.34055] 
               598                598 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 1027, number of events= 59 
   (1394 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055]  1.934e-01  1.213e+00  2.668e-01  0.725   0.4684  
Age                                                        4.267e-02  1.044e+00  1.756e-02  2.429   0.0151 *
Gendermale                                                -4.383e-02  9.571e-01  2.992e-01 -0.146   0.8835  
Hypertension.compositeno                                   8.567e-03  1.009e+00  4.178e-01  0.021   0.9836  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                    -2.315e-02  9.771e-01  3.168e-01 -0.073   0.9418  
SmokerStatusEx-smoker                                     -1.168e-01  8.898e-01  2.964e-01 -0.394   0.6936  
SmokerStatusNever smoked                                  -9.611e-01  3.825e-01  5.239e-01 -1.835   0.0666 .
Med.Statin.LLDno                                           3.743e-01  1.454e+00  2.926e-01  1.279   0.2008  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                     3.817e-01  1.465e+00  3.714e-01  1.028   0.3040  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                  -4.331e-03  9.957e-01  6.992e-03 -0.619   0.5357  
BMI                                                        8.171e-02  1.085e+00  3.459e-02  2.362   0.0182 *
MedHx_CVDyes                                               3.652e-01  1.441e+00  2.940e-01  1.242   0.2142  
stenose0-49%                                              -1.523e+01  2.419e-07  3.388e+03 -0.004   0.9964  
stenose50-70%                                             -5.088e-01  6.012e-01  1.160e+00 -0.438   0.6610  
stenose70-90%                                             -2.645e-01  7.676e-01  1.029e+00 -0.257   0.7971  
stenose90-99%                                             -2.597e-01  7.713e-01  1.029e+00 -0.252   0.8008  
stenose100% (Occlusion)                                    5.974e-01  1.817e+00  1.439e+00  0.415   0.6780  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                             -1.515e+01  2.629e-07  3.962e+03 -0.004   0.9969  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 1.213e+00  8.242e-01   0.71934     2.047
Age                                                       1.044e+00  9.582e-01   1.00828     1.080
Gendermale                                                9.571e-01  1.045e+00   0.53242     1.721
Hypertension.compositeno                                  1.009e+00  9.915e-01   0.44469     2.288
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.771e-01  1.023e+00   0.52513     1.818
SmokerStatusEx-smoker                                     8.898e-01  1.124e+00   0.49772     1.591
SmokerStatusNever smoked                                  3.825e-01  2.615e+00   0.13699     1.068
Med.Statin.LLDno                                          1.454e+00  6.878e-01   0.81941     2.580
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.465e+00  6.827e-01   0.70740     3.033
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.957e-01  1.004e+00   0.98213     1.009
BMI                                                       1.085e+00  9.215e-01   1.01402     1.161
MedHx_CVDyes                                              1.441e+00  6.940e-01   0.80970     2.564
stenose0-49%                                              2.419e-07  4.133e+06   0.00000       Inf
stenose50-70%                                             6.012e-01  1.663e+00   0.06185     5.844
stenose70-90%                                             7.676e-01  1.303e+00   0.10217     5.767
stenose90-99%                                             7.713e-01  1.297e+00   0.10256     5.800
stenose100% (Occlusion)                                   1.817e+00  5.503e-01   0.10830    30.495
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.629e-07  3.804e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.67  (se = 0.034 )
Likelihood ratio test= 23.02  on 18 df,   p=0.2
Wald test            = 20.97  on 18 df,   p=0.3
Score (logrank) test = 22.15  on 18 df,   p=0.2


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.1934 
Standard error............: 0.26675 
Odds ratio (effect size)..: 1.213 
Lower 95% CI..............: 0.719 
Upper 95% CI..............: 2.047 
T-value...................: 0.725021 
P-value...................: 0.4684393 
Sample size in model......: 1027 
Number of events..........: 59 
* Analyzing the effect of plaque proteins on [epcoronary.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34055,0.00105) [ 0.00105,3.34055] 
               598                598 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 1027, number of events= 78 
   (1394 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] -1.290e-01  8.790e-01  2.315e-01 -0.557 0.577222    
Age                                                       -8.243e-04  9.992e-01  1.506e-02 -0.055 0.956352    
Gendermale                                                 8.625e-01  2.369e+00  3.029e-01  2.848 0.004404 ** 
Hypertension.compositeno                                  -9.192e-01  3.989e-01  5.209e-01 -1.765 0.077619 .  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -1.045e-01  9.008e-01  2.759e-01 -0.379 0.704879    
SmokerStatusEx-smoker                                     -6.291e-01  5.331e-01  2.573e-01 -2.445 0.014496 *  
SmokerStatusNever smoked                                  -2.735e-01  7.607e-01  3.647e-01 -0.750 0.453241    
Med.Statin.LLDno                                           1.147e-01  1.122e+00  2.730e-01  0.420 0.674465    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     3.664e-01  1.443e+00  3.348e-01  1.095 0.273710    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -2.059e-02  9.796e-01  5.940e-03 -3.466 0.000528 ***
BMI                                                        1.350e-02  1.014e+00  3.290e-02  0.410 0.681500    
MedHx_CVDyes                                               6.867e-01  1.987e+00  2.795e-01  2.457 0.014000 *  
stenose0-49%                                              -1.584e+01  1.320e-07  3.048e+03 -0.005 0.995853    
stenose50-70%                                             -1.713e+00  1.803e-01  1.418e+00 -1.208 0.227098    
stenose70-90%                                             -1.372e-01  8.718e-01  1.022e+00 -0.134 0.893141    
stenose90-99%                                             -1.918e-01  8.255e-01  1.022e+00 -0.188 0.851135    
stenose100% (Occlusion)                                   -1.540e+01  2.059e-07  2.452e+03 -0.006 0.994990    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                              8.142e-01  2.257e+00  1.429e+00  0.570 0.568977    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 8.790e-01  1.138e+00   0.55840    1.3835
Age                                                       9.992e-01  1.001e+00   0.97011    1.0291
Gendermale                                                2.369e+00  4.221e-01   1.30849    4.2894
Hypertension.compositeno                                  3.989e-01  2.507e+00   0.14370    1.1071
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.008e-01  1.110e+00   0.52459    1.5468
SmokerStatusEx-smoker                                     5.331e-01  1.876e+00   0.32192    0.8827
SmokerStatusNever smoked                                  7.607e-01  1.315e+00   0.37224    1.5546
Med.Statin.LLDno                                          1.122e+00  8.917e-01   0.65677    1.9151
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.443e+00  6.932e-01   0.74849    2.7802
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.796e-01  1.021e+00   0.96828    0.9911
BMI                                                       1.014e+00  9.866e-01   0.95030    1.0811
MedHx_CVDyes                                              1.987e+00  5.032e-01   1.14909    3.4365
stenose0-49%                                              1.320e-07  7.576e+06   0.00000       Inf
stenose50-70%                                             1.803e-01  5.547e+00   0.01118    2.9061
stenose70-90%                                             8.718e-01  1.147e+00   0.11772    6.4559
stenose90-99%                                             8.255e-01  1.211e+00   0.11137    6.1183
stenose100% (Occlusion)                                   2.059e-07  4.857e+06   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.257e+00  4.430e-01   0.13703   37.1835
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.726  (se = 0.028 )
Likelihood ratio test= 49.9  on 18 df,   p=8e-05
Wald test            = 43.46  on 18 df,   p=7e-04
Score (logrank) test = 47.19  on 18 df,   p=2e-04


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: -0.129025 
Standard error............: 0.231458 
Odds ratio (effect size)..: 0.879 
Lower 95% CI..............: 0.558 
Upper 95% CI..............: 1.384 
T-value...................: -0.557447 
P-value...................: 0.577222 
Sample size in model......: 1027 
Number of events..........: 78 
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34055,0.00105) [ 0.00105,3.34055] 
               598                598 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 1027, number of events= 33 
   (1394 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] -2.044e-01  8.152e-01  3.583e-01 -0.570  0.56836    
Age                                                        6.640e-02  1.069e+00  2.655e-02  2.501  0.01238 *  
Gendermale                                                 1.258e+00  3.520e+00  5.565e-01  2.261  0.02373 *  
Hypertension.compositeno                                  -1.774e+01  1.978e-08  4.017e+03 -0.004  0.99648    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -3.835e-02  9.624e-01  4.261e-01 -0.090  0.92829    
SmokerStatusEx-smoker                                     -5.672e-01  5.671e-01  4.021e-01 -1.411  0.15834    
SmokerStatusNever smoked                                  -4.302e-01  6.504e-01  6.158e-01 -0.699  0.48479    
Med.Statin.LLDno                                           8.681e-02  1.091e+00  4.156e-01  0.209  0.83454    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     1.130e+00  3.096e+00  4.161e-01  2.716  0.00661 ** 
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -3.395e-02  9.666e-01  9.369e-03 -3.624  0.00029 ***
BMI                                                        8.410e-02  1.088e+00  5.118e-02  1.643  0.10031    
MedHx_CVDyes                                               7.683e-01  2.156e+00  4.605e-01  1.668  0.09528 .  
stenose0-49%                                              -2.031e+01  1.509e-09  2.713e+04 -0.001  0.99940    
stenose50-70%                                             -1.041e+00  3.531e-01  1.236e+00 -0.842  0.39970    
stenose70-90%                                             -1.480e+00  2.276e-01  1.069e+00 -1.385  0.16609    
stenose90-99%                                             -1.069e+00  3.434e-01  1.054e+00 -1.014  0.31055    
stenose100% (Occlusion)                                   -1.957e+01  3.154e-09  1.974e+04 -0.001  0.99921    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.939e+01  3.802e-09  3.407e+04 -0.001  0.99955    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 8.152e-01  1.227e+00   0.40392    1.6451
Age                                                       1.069e+00  9.358e-01   1.01447    1.1257
Gendermale                                                3.520e+00  2.841e-01   1.18264   10.4751
Hypertension.compositeno                                  1.978e-08  5.054e+07   0.00000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.624e-01  1.039e+00   0.41748    2.2185
SmokerStatusEx-smoker                                     5.671e-01  1.763e+00   0.25789    1.2471
SmokerStatusNever smoked                                  6.504e-01  1.538e+00   0.19453    2.1744
Med.Statin.LLDno                                          1.091e+00  9.168e-01   0.48298    2.4631
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    3.096e+00  3.230e-01   1.36967    6.9988
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.666e-01  1.035e+00   0.94903    0.9845
BMI                                                       1.088e+00  9.193e-01   0.98393    1.2025
MedHx_CVDyes                                              2.156e+00  4.638e-01   0.87427    5.3170
stenose0-49%                                              1.509e-09  6.625e+08   0.00000       Inf
stenose50-70%                                             3.531e-01  2.832e+00   0.03132    3.9813
stenose70-90%                                             2.276e-01  4.394e+00   0.02802    1.8490
stenose90-99%                                             3.434e-01  2.912e+00   0.04351    2.7102
stenose100% (Occlusion)                                   3.154e-09  3.170e+08   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             3.802e-09  2.630e+08   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.839  (se = 0.031 )
Likelihood ratio test= 60.15  on 18 df,   p=2e-06
Wald test            = 20.83  on 18 df,   p=0.3
Score (logrank) test = 56.03  on 18 df,   p=9e-06


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: -0.204374 
Standard error............: 0.358254 
Odds ratio (effect size)..: 0.815 
Lower 95% CI..............: 0.404 
Upper 95% CI..............: 1.645 
T-value...................: -0.570471 
P-value...................: 0.568358 
Sample size in model......: 1027 
Number of events..........: 33 

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)
object 'head.style' not found

30-days follow-up

MODEL 1

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

MODEL 2

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

90-days follow-up

MODEL 1

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

MODEL 2

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

Correlations

All biomarkers

We correlated serum and plaque levels of the biomarkers.


# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")
Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
  Use `force = TRUE` to force installation
library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_pg_ug_2015_rank",
                                               TRAITS.BIN, TRAITS.CON.RANK)
                                    )


AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
str(AEDB.CEA.temp)
tibble [2,421 × 8] (S3: tbl_df/tbl/data.frame)
 $ MCP1_pg_ug_2015_rank: num [1:2421] NA 0.937 2.158 1.209 1.996 ...
 $ CalcificationPlaque : num [1:2421] 1 1 1 1 1 2 2 1 2 1 ...
 $ CollagenPlaque      : num [1:2421] 1 2 2 2 2 1 NA 2 2 2 ...
 $ Fat10Perc           : num [1:2421] 2 2 2 2 2 2 2 2 2 2 ...
 $ IPH                 : num [1:2421] 2 2 2 2 1 2 2 2 2 2 ...
 $ Macrophages_rank    : num [1:2421] 1.388 1.12 1.365 0.721 0.396 ...
 $ SMC_rank            : num [1:2421] 1.67023 1.13089 0.00295 1.42623 1.2689 ...
 $ VesselDensity_rank  : num [1:2421] -0.529 -0.977 -0.774 0.716 1.099 ...
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")
Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))



# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)


# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}


chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)



# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:

# ggpairs(AEDB.CEA, 
#         columns = c("MCP1_rank", "MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK), 
#         columnLabels = c("MCP1 (serum)", "MCP1", 
#                          "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
#         method = c("spearman"),
#         # ggplot2::aes(colour = Gender),
#         progress = FALSE) 

ggpairs(AEDB.CEA,
        columns = c("MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK),
        columnLabels = c("MCP1",
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE)
Extra arguments: 'method' are being ignored.  If these are meant to be aesthetics, submit them using the 'mapping' variable within ggpairs with ggplot2::aes or ggplot2::aes_string.

Circulating MCP1

Finally, we explored in a sub-sample, where circulating MCP-1 levels are available, the following:

  1. A correlation between MCP-1 levels in the plaque and circulating MCP-1 levels
  2. Associations of circulating MCP-1 levels with plaque vulnerability characteristics
  3. Associations of circulating MCP-1 levels with the status of the plaque in terms of presence of symptoms (symptomatic vs. asymptomatic)
  4. Associations of circulating MCP-1 levels with the primary composite endpoint of secondary cardiovascular events.

NOT AVAILABLE YET


# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_rank", 
                                     TRAITS.BIN, TRAITS.CON.RANK, 
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.serum.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers_serum.rank <- round(cor(AEDB.CEA.matrix.serum.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_serum_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.serum.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_serum.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_serum_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}


corr_biomarkers_serum.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_serum.rank, corr_biomarkers_serum_p.rank))
DT::datatable(corr_biomarkers_serum.rank.df)

# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}

chart.Correlation.new(AEDB.CEA.matrix.serum.RANK, method = "spearman", histogram = TRUE, pch = 3)


# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:
ggpairs(AEDB.CEA,
        columns = c("MCP1_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"), 
        columnLabels = c("MCP1 (serum)", 
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE) 

Additional figures

Age and sex

We want to create per-age-group figures.

  • Box and Whisker plot for MCP-1 plaque levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
  • Box and Whisker plot for MCP-1 serum levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
  • Scatter plot of the correlation and regression line between MCP-1 levels in plaque (y axis) and serum (x axis).
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
                                                     Age >= 55  & Age <= 64 ~ "55-64",
                                                     Age >= 65  & Age <= 74 ~ "65-74",
                                                     Age >= 75  & Age <= 84 ~ "75-84",
                                                     Age >= 85 ~ "85+"))) 

AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
                                                        Age >= 55  & Age <= 64 & Gender == "male"~ "55-64 males",
                                                        Age >= 65  & Age <= 74 & Gender == "male"~ "65-74 males",
                                                        Age >= 75  & Age <= 84 & Gender == "male"~ "75-84 males",
                                                        Age >= 85 & Gender == "male"~ "85+ males",
                                                        Age < 55 & Gender == "female" ~ "<55 females" ,
                                                        Age >= 55  & Age <= 64 & Gender == "female"~ "55-64 females ",
                                                        Age >= 65  & Age <= 74 & Gender == "female"~ "65-74 females",
                                                        Age >= 75  & Age <= 84 & Gender == "female"~ "75-84 females",
                                                        Age >= 85 & Gender == "female"~ "85+ females")))

table(AEDB.CEA$AgeGroup, AEDB.CEA$Gender)
       
        female male
  <55       45   98
  55-64    193  410
  65-74    264  687
  75-84    202  438
  85+       34   50
table(AEDB.CEA$AgeGroupSex)

   <55 females      <55 males 55-64 females     55-64 males  65-74 females    65-74 males  75-84 females    75-84 males    85+ females 
            45             98            193            410            264            687            202            438             34 
     85+ males 
            50 

Now we can draw some graphs of serum/plaque MCP1 levels per sex and age group.

Inverse-rank transformed data

MCP1 plaque levels


# ?ggpubr::ggboxplot()

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

MCP1 serum levels

NOT AVAILABLE YET

# compare_means(MCP1_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("Gender"),
                  y = "MCP1_rank",
                  xlab = "Gender",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("AgeGroup"),
                  y = "MCP1_rank",
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

Raw data

Simalarly but now for the raw data as median ± interquartile range.

MCP1 plaque levels


# ?ggpubr::ggboxplot()

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+

  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+

  # stat_compare_means(method = "kruskal.test")

MCP1 serum levels

NOT AVAILABLE YET

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1", 
                  xlab = "Gender",
                  ylab = "MCP1 serum [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 serum [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

Circulating vs. plaque MCP1 levels

We will also make a nice correlation plot between serum and plaque MCP1 levels.

NOT AVAILABLE YET


ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_pg_ug_2015",
                  y = "MCP1", 
                  xlab = "MCP1 plaque [pg/ug]",
                  ylab = "MCP1 serum [pg/mL]",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(8,750))

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_pg_ug_2015_rank",
                  y = "MCP1_rank", 
                  xlab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(2,3))

Plaque vs. plaque MCP1 levels

We will also make a nice correlation plot between the two experiments of plaque MCP1 levels.

AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
summary(AEDB.CEA$MCP1)
summary(AEDB.CEA$MCP1_pg_ug_2015)

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1", 
                  y = "MCP1_pg_ug_2015",
                  xlab = "MCP1 plaque [pg/mL] (exp. no. 1)",
                  ylab = "MCP1 plaque [pg/ug] (exp. no. 2)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_rank", 
                  y = "MCP1_pg_ug_2015_rank",
                  xlab = "MCP1 plaque [pg/mL]\n(INRT,  exp. no. 1)",
                  ylab = "MCP1 plaque [pg/ug]\n(INRT,  exp. no. 2)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))

Symptoms

We want to create per-symptom figures.

library(dplyr)

table(AEDB.CEA$AgeGroup, AEDB.CEA$AsymptSympt2G)
       
        Asymptomatic Symptomatic
  <55             24         119
  55-64           76         527
  65-74          124         827
  75-84           43         597
  85+              3          81
table(AEDB.CEA$Gender, AEDB.CEA$AsymptSympt2G)
        
         Asymptomatic Symptomatic
  female           64         674
  male            206        1477
table(AEDB.CEA$AsymptSympt2G)

Asymptomatic  Symptomatic 
         270         2151 

Now we can draw some graphs of serum/plaque MCP1 levels per symptom group.


# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_pg_ug_2015_rank",
                  title = "MCP1 plaque [pg/ug] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 plaque [pg/ug]\n inverse-rank transformation",
                  color = "AsymptSympt2G", 
                  palette = c(uithof_color[16], uithof_color[23]),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_rank",
                  title = "MCP1 serum [pg/mL] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 serum [pg/mL]\n inverse-rank transformation",
                  color = "AsymptSympt2G", 
                  palette = c(uithof_color[16], uithof_color[23]),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
rm(p1)

Forest plots

We would also like to visualize the multivariable analyses results.

library(ggplot2)
library(openxlsx)
model1_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"))
model2_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"))
model1_mcp1$model <- "univariate"
model2_mcp1$model <- "multivariate"

models_mcp1 <- rbind(model1_mcp1, model2_mcp1)
models_mcp1
NA
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]))

fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
  geom_pointrange() + 
  geom_hline(yintercept=1, lty=2) +  # add a dotted line at x=1 after flip
  coord_flip() +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  theme(text = element_text(size=14)) +
  ggtitle("Plaque MCP-1 levels (1 SD increment)") +
  theme_minimal()  # use a white background
print(fp)

rm(fp)
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_rank"]))

fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
  geom_pointrange() + 
  geom_hline(yintercept=1, lty=2) +  # add a dotted line at x=1 after flip
  coord_flip() +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  theme(text = element_text(size=14)) +
  ggtitle("Serum MCP-1 levels (1 SD increment)") +
  theme_minimal()  # use a white background
print(fp)
rm(fp)

MCP1 vs. cytokines plaque levels correlations

We will plot the correlations of other cytokine plaque levels to the MCP1 plaque levels. These include:

  • IL2
  • IL4
  • IL5
  • IL6
  • IL8
  • IL9
  • IL10
  • IL12
  • IL13
  • IL21
  • INFG
  • TNFA
  • MIF
  • MCP1
  • MIP1a
  • RANTES
  • MIG
  • IP10
  • Eotaxin1
  • TARC
  • PARC
  • MDC
  • OPG
  • sICAM1
  • VEGFA
  • TGFB

In addition we will look at three metalloproteinases which were measured using an activity assay.

  • MMP2
  • MMP8
  • MMP9

The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the MCP1 plaque levels.

We will set the measurements that yielded ‘0’ to NA, as it is unlikely that any protein ever has exactly 0 copies. The ‘0’ yielded during the experiment are due to the limits of the detection.

Prepare data

cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21", 
               "INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1", 
               "TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")

# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"


proteins_of_interest <- c(cytokines, metalloproteinases)

proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))


# make variables numerics()
AEDB.CEA <- AEDB.CEA %>%
  mutate_each(funs(as.numeric), proteins_of_interest)
funs() is soft deprecated as of dplyr 0.8.0
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once per session.Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(proteins_of_interest)` instead of `proteins_of_interest` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
  
for(PROTEIN in 1:length(proteins_of_interest)){

  # UCORBIOGSAqc$Z <- NULL
  var.temp.rank = proteins_of_interest_rank[PROTEIN]
  var.temp = proteins_of_interest[PROTEIN]
  
  cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
  cat(paste0("* changing ", var.temp, " to numeric.\n"))

  # AEDB.CEA <-  AEDB.CEA %>% mutate(AEDB.CEA[,var.temp] == replace(AEDB.CEA[,var.temp], AEDB.CEA[,var.temp]==0, NA))

  AEDB.CEA[,var.temp][AEDB.CEA[,var.temp]==0.000000]=NA

  cat(paste0("* standardising ", var.temp, 
             " (mean: ",round(mean(!is.na(AEDB.CEA[,var.temp])), digits = 6),
             ", n = ",sum(!is.na(AEDB.CEA[,var.temp])),").\n"))
  
  AEDB.CEA <- AEDB.CEA %>%
      mutate_at(vars(var.temp), 
        # list(Z = ~ (AEDB.CEA[,var.temp] - mean(AEDB.CEA[,var.temp], na.rm = TRUE))/sd(AEDB.CEA[,var.temp], na.rm = TRUE))
        list(RANK = ~ qnorm((rank(AEDB.CEA[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA[,var.temp]))))
      )
  # str(UCORBIOGSAqc$Z)
  cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
  AEDB.CEA[,var.temp.rank] <- NULL
  names(AEDB.CEA)[names(AEDB.CEA) == "RANK"] <- var.temp.rank
}

Selecting IL2 and standardising: IL2_rank.
* changing IL2 to numeric.
* standardising IL2 (mean: 0.180091, n = 436).
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(var.temp)` instead of `var.temp` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
* renaming RANK to IL2_rank.

Selecting IL4 and standardising: IL4_rank.
* changing IL4 to numeric.
* standardising IL4 (mean: 0.167699, n = 406).
* renaming RANK to IL4_rank.

Selecting IL5 and standardising: IL5_rank.
* changing IL5 to numeric.
* standardising IL5 (mean: 0.178439, n = 432).
* renaming RANK to IL5_rank.

Selecting IL6 and standardising: IL6_rank.
* changing IL6 to numeric.
* standardising IL6 (mean: 0.188352, n = 456).
* renaming RANK to IL6_rank.

Selecting IL8 and standardising: IL8_rank.
* changing IL8 to numeric.
* standardising IL8 (mean: 0.182156, n = 441).
* renaming RANK to IL8_rank.

Selecting IL9 and standardising: IL9_rank.
* changing IL9 to numeric.
* standardising IL9 (mean: 0.206526, n = 500).
* renaming RANK to IL9_rank.

Selecting IL10 and standardising: IL10_rank.
* changing IL10 to numeric.
* standardising IL10 (mean: 0.159025, n = 385).
* renaming RANK to IL10_rank.

Selecting IL12 and standardising: IL12_rank.
* changing IL12 to numeric.
* standardising IL12 (mean: 0.168112, n = 407).
* renaming RANK to IL12_rank.

Selecting IL13 and standardising: IL13_rank.
* changing IL13 to numeric.
* standardising IL13 (mean: 0.232962, n = 564).
* renaming RANK to IL13_rank.

Selecting IL21 and standardising: IL21_rank.
* changing IL21 to numeric.
* standardising IL21 (mean: 0.233375, n = 565).
* renaming RANK to IL21_rank.

Selecting INFG and standardising: INFG_rank.
* changing INFG to numeric.
* standardising INFG (mean: 0.179265, n = 434).
* renaming RANK to INFG_rank.

Selecting TNFA and standardising: TNFA_rank.
* changing TNFA to numeric.
* standardising TNFA (mean: 0.163569, n = 396).
* renaming RANK to TNFA_rank.

Selecting MIF and standardising: MIF_rank.
* changing MIF to numeric.
* standardising MIF (mean: 0.233375, n = 565).
* renaming RANK to MIF_rank.

Selecting MCP1 and standardising: MCP1_rank.
* changing MCP1 to numeric.
* standardising MCP1 (mean: 0.229657, n = 556).
* renaming RANK to MCP1_rank.

Selecting MIP1a and standardising: MIP1a_rank.
* changing MIP1a to numeric.
* standardising MIP1a (mean: 0.211896, n = 513).
* renaming RANK to MIP1a_rank.

Selecting RANTES and standardising: RANTES_rank.
* changing RANTES to numeric.
* standardising RANTES (mean: 0.228831, n = 554).
* renaming RANK to RANTES_rank.

Selecting MIG and standardising: MIG_rank.
* changing MIG to numeric.
* standardising MIG (mean: 0.227179, n = 550).
* renaming RANK to MIG_rank.

Selecting IP10 and standardising: IP10_rank.
* changing IP10 to numeric.
* standardising IP10 (mean: 0.206113, n = 499).
* renaming RANK to IP10_rank.

Selecting Eotaxin1 and standardising: Eotaxin1_rank.
* changing Eotaxin1 to numeric.
* standardising Eotaxin1 (mean: 0.233375, n = 565).
* renaming RANK to Eotaxin1_rank.

Selecting TARC and standardising: TARC_rank.
* changing TARC to numeric.
* standardising TARC (mean: 0.200743, n = 486).
* renaming RANK to TARC_rank.

Selecting PARC and standardising: PARC_rank.
* changing PARC to numeric.
* standardising PARC (mean: 0.233375, n = 565).
* renaming RANK to PARC_rank.

Selecting MDC and standardising: MDC_rank.
* changing MDC to numeric.
* standardising MDC (mean: 0.209831, n = 508).
* renaming RANK to MDC_rank.

Selecting OPG and standardising: OPG_rank.
* changing OPG to numeric.
* standardising OPG (mean: 0.232962, n = 564).
* renaming RANK to OPG_rank.

Selecting sICAM1 and standardising: sICAM1_rank.
* changing sICAM1 to numeric.
* standardising sICAM1 (mean: 0.233375, n = 565).
* renaming RANK to sICAM1_rank.

Selecting VEGFA and standardising: VEGFA_rank.
* changing VEGFA to numeric.
* standardising VEGFA (mean: 0.20157, n = 488).
* renaming RANK to VEGFA_rank.

Selecting TGFB and standardising: TGFB_rank.
* changing TGFB to numeric.
* standardising TGFB (mean: 0.23007, n = 557).
* renaming RANK to TGFB_rank.

Selecting MMP2 and standardising: MMP2_rank.
* changing MMP2 to numeric.
* standardising MMP2 (mean: 0.232135, n = 562).
* renaming RANK to MMP2_rank.

Selecting MMP8 and standardising: MMP8_rank.
* changing MMP8 to numeric.
* standardising MMP8 (mean: 0.232135, n = 562).
* renaming RANK to MMP8_rank.

Selecting MMP9 and standardising: MMP9_rank.
* changing MMP9 to numeric.
* standardising MMP9 (mean: 0.231722, n = 561).
* renaming RANK to MMP9_rank.
# rm(var.temp, var.temp.rank)

Visualize transformations

We will just visualize these transformations.

proteins_of_interest_rank_mcp1 <- c("MCP1_pg_ug_2015_rank", proteins_of_interest_rank)

proteins_of_interest_mcp1 <- c("MCP1_pg_ug_2015", proteins_of_interest)

for(PROTEIN in proteins_of_interest_mcp1){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "",
                    ggtheme = theme_minimal())
  print(p1)
  
}
Plotting protein MCP1_pg_ug_2015.
Using `bins = 30` by default. Pick better value with the argument `bins`.
Plotting protein IL2.
Plotting protein IL4.
Plotting protein IL5.
Plotting protein IL6.
Plotting protein IL8.
Plotting protein IL9.
Plotting protein IL10.
Plotting protein IL12.
Plotting protein IL13.
Plotting protein IL21.
Plotting protein INFG.
Plotting protein TNFA.
Plotting protein MIF.
Plotting protein MCP1.
Plotting protein MIP1a.
Plotting protein RANTES.
Plotting protein MIG.
Plotting protein IP10.
Plotting protein Eotaxin1.
Plotting protein TARC.
Plotting protein PARC.
Plotting protein MDC.
Plotting protein OPG.
Plotting protein sICAM1.
Plotting protein VEGFA.
Plotting protein TGFB.
Plotting protein MMP2.
Plotting protein MMP8.
Plotting protein MMP9.

for(PROTEIN in proteins_of_interest_rank_mcp1){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "inverse-normal transformation",
                    ggtheme = theme_minimal())
  print(p1)
  
}
Plotting protein MCP1_pg_ug_2015_rank.
Using `bins = 30` by default. Pick better value with the argument `bins`.
Plotting protein IL2_rank.
Plotting protein IL4_rank.
Plotting protein IL5_rank.
Plotting protein IL6_rank.
Plotting protein IL8_rank.
Plotting protein IL9_rank.
Plotting protein IL10_rank.
Plotting protein IL12_rank.
Plotting protein IL13_rank.
Plotting protein IL21_rank.
Plotting protein INFG_rank.
Plotting protein TNFA_rank.
Plotting protein MIF_rank.
Plotting protein MCP1_rank.
Plotting protein MIP1a_rank.
Plotting protein RANTES_rank.
Plotting protein MIG_rank.
Plotting protein IP10_rank.
Plotting protein Eotaxin1_rank.
Plotting protein TARC_rank.
Plotting protein PARC_rank.
Plotting protein MDC_rank.
Plotting protein OPG_rank.
Plotting protein sICAM1_rank.
Plotting protein VEGFA_rank.
Plotting protein TGFB_rank.
Plotting protein MMP2_rank.
Plotting protein MMP8_rank.
Plotting protein MMP9_rank.

NA

Correlations

Here we calculate correlations between MCP1_pg_ug_2015 and 28 other cytokines (including MCP1 as measured in experiment 1. We use Spearman’s test, thus, correlations a given in rho. Please note the indications of measurement methods:

  • L: LUMINEX
  • E: ELISA
  • a: activity assay
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")
Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
  Use `force = TRUE` to force installation
library(ggcorrplot)

# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c(proteins_of_interest_rank_mcp1)
                                    )

# str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

rename_proteins_of_interest_mcp1 <- c("MCP1 (L, exp2)", 
                                    "IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", 
                                    "IL13 (L)", "IL21 (L)", 
                                    "INFG", "TNFA", "MIF (L)", 
                                    "MCP1 (L, exp1)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)", 
                                    "Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)", 
                                    "OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
    )
}

corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)


names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"

DT::datatable(corr_biomarkers.rank.df)


fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           tl.cex = 6,
           # xlab = c("MCP1"),
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())
Saving 7.29 x 4.51 in image

rm(p1)

While visually actractive we are not necessarily interested in the correlations between all the cytokines, rather of MCP1 with other cytokines only.

temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp2)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold
[1] 2.763428
p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
          fill = "CytokineY",               # change fill color by cyl
          # color = "white",            # Set bar border colors to white
          palette = uithof_color,            # jco journal color palett. see ?ggpar
          xlab = "Cytokine",
          ylab = expression("Spearman's"~italic(rho)),
          sort.val = "desc",          # Sort the value in dscending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 45, # Rotate vertically x axis texts
          cex = 0.8
          )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 9),
        axis.text.y = element_text(size = 9)) 


rm(p1)

Another version - problably not good.


p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
           color = "CytokineY",                                # Color by groups
           palette = uithof_color, # Custom color palette
           xlab = "Cytokine",
           ylab = expression(log[10]~"("~italic(p)~")-value"),
           ylim = c(0, 6),
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           rotate = FALSE,                                # Rotate vertically
           # group = "CytokineY",                                # Order by groups
           dot.size = 8,                                 # Large dot size
           label = round(temp$SpearmanRho, digits = 3),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 8, 
                             vjust = 0.5)                   
           )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 9),
        axis.text.y = element_text(size = 9))


# rm(temp, p1)

MCP1 vs. cytokines plaque levels lm()

Model 1

In this model we correct for Age and Gender.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines traits as a function of serum/plaque MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of MCP1_pg_ug_2015_rank.

- processing IL2_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
    -0.1871  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0138 -0.7263 -0.1541  0.4375  3.1658 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)        -0.126985   0.448729  -0.283    0.777
currentDF[, TRAIT]  0.068417   0.057955   1.181    0.239
Age                -0.001586   0.006519  -0.243    0.808
Gendermale          0.069623   0.123140   0.565    0.572

Residual standard error: 1.045 on 337 degrees of freedom
Multiple R-squared:  0.005043,  Adjusted R-squared:  -0.003814 
F-statistic: 0.5694 on 3 and 337 DF,  p-value: 0.6355

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL2_rank 
Effect size...............: 0.068417 
Standard error............: 0.057955 
Odds ratio (effect size)..: 1.071 
Lower 95% CI..............: 0.956 
Upper 95% CI..............: 1.2 
T-value...................: 1.180503 
P-value...................: 0.2386327 
R^2.......................: 0.005043 
Adjusted r^2..............: -0.003814 
Sample size of AE DB......: 2421 
Sample size of model......: 341 
Missing data %............: 85.91491 

- processing IL4_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
    -0.1461  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1833 -0.7351 -0.1788  0.4975  3.2187 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)        -0.273395   0.461377  -0.593    0.554
currentDF[, TRAIT]  0.022799   0.060030   0.380    0.704
Age                 0.001119   0.006711   0.167    0.868
Gendermale          0.074316   0.129082   0.576    0.565

Residual standard error: 1.033 on 312 degrees of freedom
Multiple R-squared:  0.001415,  Adjusted R-squared:  -0.008186 
F-statistic: 0.1474 on 3 and 312 DF,  p-value: 0.9313

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL4_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL4_rank 
Effect size...............: 0.022799 
Standard error............: 0.06003 
Odds ratio (effect size)..: 1.023 
Lower 95% CI..............: 0.91 
Upper 95% CI..............: 1.151 
T-value...................: 0.379795 
P-value...................: 0.7043557 
R^2.......................: 0.001415 
Adjusted r^2..............: -0.008186 
Sample size of AE DB......: 2421 
Sample size of model......: 316 
Missing data %............: 86.94754 

- processing IL5_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
    -0.1481  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1489 -0.7125 -0.1506  0.4735  3.2342 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -0.1984677  0.4340793  -0.457    0.648
currentDF[, TRAIT]  0.0457356  0.0561918   0.814    0.416
Age                -0.0003759  0.0063395  -0.059    0.953
Gendermale          0.1114000  0.1206326   0.923    0.356

Residual standard error: 1.017 on 334 degrees of freedom
Multiple R-squared:  0.00427,   Adjusted R-squared:  -0.004674 
F-statistic: 0.4774 on 3 and 334 DF,  p-value: 0.6982

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL5_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL5_rank 
Effect size...............: 0.045736 
Standard error............: 0.056192 
Odds ratio (effect size)..: 1.047 
Lower 95% CI..............: 0.938 
Upper 95% CI..............: 1.169 
T-value...................: 0.813918 
P-value...................: 0.4162716 
R^2.......................: 0.00427 
Adjusted r^2..............: -0.004674 
Sample size of AE DB......: 2421 
Sample size of model......: 338 
Missing data %............: 86.03883 

- processing IL6_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
    -0.2321  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0907 -0.7289 -0.1173  0.4877  3.3012 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)        -0.097637   0.467842  -0.209    0.835
currentDF[, TRAIT]  0.047978   0.058966   0.814    0.416
Age                -0.003265   0.006785  -0.481    0.631
Gendermale          0.122472   0.127312   0.962    0.337

Residual standard error: 1.095 on 348 degrees of freedom
Multiple R-squared:  0.004861,  Adjusted R-squared:  -0.003717 
F-statistic: 0.5667 on 3 and 348 DF,  p-value: 0.6373

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL6_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL6_rank 
Effect size...............: 0.047978 
Standard error............: 0.058966 
Odds ratio (effect size)..: 1.049 
Lower 95% CI..............: 0.935 
Upper 95% CI..............: 1.178 
T-value...................: 0.813645 
P-value...................: 0.4164044 
R^2.......................: 0.004861 
Adjusted r^2..............: -0.003717 
Sample size of AE DB......: 2421 
Sample size of model......: 352 
Missing data %............: 85.46055 

- processing IL8_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age  
           0.40941             0.11232            -0.01017  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2363 -0.7169 -0.0887  0.4757  3.3067 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)  
(Intercept)         0.301998   0.475678   0.635   0.5259  
currentDF[, TRAIT]  0.103929   0.059601   1.744   0.0821 .
Age                -0.009993   0.006810  -1.467   0.1432  
Gendermale          0.136446   0.129655   1.052   0.2934  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.09 on 338 degrees of freedom
Multiple R-squared:  0.01861,   Adjusted R-squared:  0.009896 
F-statistic: 2.136 on 3 and 338 DF,  p-value: 0.09545

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL8_rank 
Effect size...............: 0.103929 
Standard error............: 0.059601 
Odds ratio (effect size)..: 1.11 
Lower 95% CI..............: 0.987 
Upper 95% CI..............: 1.247 
T-value...................: 1.743739 
P-value...................: 0.08211378 
R^2.......................: 0.018606 
Adjusted r^2..............: 0.009896 
Sample size of AE DB......: 2421 
Sample size of model......: 342 
Missing data %............: 85.87361 

- processing IL9_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
           -0.3089              0.1200  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1515 -0.7544 -0.1109  0.5568  3.3009 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)  
(Intercept)         0.169209   0.459466   0.368   0.7129  
currentDF[, TRAIT]  0.110359   0.058924   1.873   0.0619 .
Age                -0.008347   0.006647  -1.256   0.2100  
Gendermale          0.125953   0.123899   1.017   0.3100  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.118 on 377 degrees of freedom
Multiple R-squared:  0.01761,   Adjusted R-squared:  0.00979 
F-statistic: 2.252 on 3 and 377 DF,  p-value: 0.08189

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL9_rank 
Effect size...............: 0.110359 
Standard error............: 0.058924 
Odds ratio (effect size)..: 1.117 
Lower 95% CI..............: 0.995 
Upper 95% CI..............: 1.253 
T-value...................: 1.872907 
P-value...................: 0.06185486 
R^2.......................: 0.017607 
Adjusted r^2..............: 0.00979 
Sample size of AE DB......: 2421 
Sample size of model......: 381 
Missing data %............: 84.2627 

- processing IL10_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
    -0.1469  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0945 -0.7232 -0.1486  0.4485  3.2098 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -0.2746364  0.4684805  -0.586    0.558
currentDF[, TRAIT]  0.0849810  0.0628692   1.352    0.177
Age                 0.0005042  0.0068648   0.073    0.942
Gendermale          0.1389622  0.1319915   1.053    0.293

Residual standard error: 1.04 on 297 degrees of freedom
Multiple R-squared:  0.008715,  Adjusted R-squared:  -0.001298 
F-statistic: 0.8704 on 3 and 297 DF,  p-value: 0.4568

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL10_rank 
Effect size...............: 0.084981 
Standard error............: 0.062869 
Odds ratio (effect size)..: 1.089 
Lower 95% CI..............: 0.962 
Upper 95% CI..............: 1.231 
T-value...................: 1.351712 
P-value...................: 0.1774961 
R^2.......................: 0.008715 
Adjusted r^2..............: -0.001298 
Sample size of AE DB......: 2421 
Sample size of model......: 301 
Missing data %............: 87.56712 

- processing IL12_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
    -0.1817  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0695 -0.7182 -0.1613  0.4479  3.2230 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)        -0.395537   0.458264  -0.863    0.389
currentDF[, TRAIT]  0.077047   0.059795   1.289    0.199
Age                 0.002087   0.006733   0.310    0.757
Gendermale          0.106411   0.127040   0.838    0.403

Residual standard error: 1.032 on 312 degrees of freedom
Multiple R-squared:  0.00732,   Adjusted R-squared:  -0.002225 
F-statistic: 0.7669 on 3 and 312 DF,  p-value: 0.5133

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL12_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL12_rank 
Effect size...............: 0.077047 
Standard error............: 0.059795 
Odds ratio (effect size)..: 1.08 
Lower 95% CI..............: 0.961 
Upper 95% CI..............: 1.214 
T-value...................: 1.288522 
P-value...................: 0.198519 
R^2.......................: 0.00732 
Adjusted r^2..............: -0.002225 
Sample size of AE DB......: 2421 
Sample size of model......: 316 
Missing data %............: 86.94754 

- processing IL13_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
           -0.3001              0.1061  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1459 -0.7317 -0.1060  0.5223  3.2560 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)  
(Intercept)         0.118249   0.413981   0.286   0.7753  
currentDF[, TRAIT]  0.095865   0.053668   1.786   0.0748 .
Age                -0.007198   0.006004  -1.199   0.2312  
Gendermale          0.099873   0.112423   0.888   0.3748  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.082 on 430 degrees of freedom
Multiple R-squared:  0.01406,   Adjusted R-squared:  0.007179 
F-statistic: 2.044 on 3 and 430 DF,  p-value: 0.1071

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL13_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL13_rank 
Effect size...............: 0.095865 
Standard error............: 0.053668 
Odds ratio (effect size)..: 1.101 
Lower 95% CI..............: 0.991 
Upper 95% CI..............: 1.223 
T-value...................: 1.786256 
P-value...................: 0.0747623 
R^2.......................: 0.014058 
Adjusted r^2..............: 0.007179 
Sample size of AE DB......: 2421 
Sample size of model......: 434 
Missing data %............: 82.07352 

- processing IL21_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
           -0.2990              0.1013  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1326 -0.7218 -0.0901  0.5073  3.2716 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)  
(Intercept)         0.126296   0.412433   0.306   0.7596  
currentDF[, TRAIT]  0.090108   0.054130   1.665   0.0967 .
Age                -0.007244   0.005975  -1.212   0.2260  
Gendermale          0.094794   0.112227   0.845   0.3988  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.081 on 431 degrees of freedom
Multiple R-squared:  0.01303,   Adjusted R-squared:  0.006158 
F-statistic: 1.896 on 3 and 431 DF,  p-value: 0.1295

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL21_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL21_rank 
Effect size...............: 0.090108 
Standard error............: 0.05413 
Odds ratio (effect size)..: 1.094 
Lower 95% CI..............: 0.984 
Upper 95% CI..............: 1.217 
T-value...................: 1.664666 
P-value...................: 0.09670626 
R^2.......................: 0.013028 
Adjusted r^2..............: 0.006158 
Sample size of AE DB......: 2421 
Sample size of model......: 435 
Missing data %............: 82.03222 

- processing INFG_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale  
           -0.3374              0.1641              0.1825  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0061 -0.7195 -0.1358  0.4739  3.1951 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)   
(Intercept)        -0.153946   0.461624  -0.333  0.73898   
currentDF[, TRAIT]  0.161772   0.061892   2.614  0.00936 **
Age                -0.002728   0.006680  -0.408  0.68327   
Gendermale          0.183410   0.127424   1.439  0.15099   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.06 on 332 degrees of freedom
Multiple R-squared:  0.02468,   Adjusted R-squared:  0.01586 
F-statistic:   2.8 on 3 and 332 DF,  p-value: 0.04005

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' INFG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: INFG_rank 
Effect size...............: 0.161772 
Standard error............: 0.061892 
Odds ratio (effect size)..: 1.176 
Lower 95% CI..............: 1.041 
Upper 95% CI..............: 1.327 
T-value...................: 2.613754 
P-value...................: 0.009363151 
R^2.......................: 0.024678 
Adjusted r^2..............: 0.015864 
Sample size of AE DB......: 2421 
Sample size of model......: 336 
Missing data %............: 86.12144 

- processing TNFA_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
    -0.1969  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0969 -0.7002 -0.1285  0.4529  3.2669 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)        -0.121674   0.461335  -0.264    0.792
currentDF[, TRAIT]  0.071417   0.060266   1.185    0.237
Age                -0.002395   0.006720  -0.356    0.722
Gendermale          0.132059   0.127121   1.039    0.300

Residual standard error: 1.022 on 302 degrees of freedom
Multiple R-squared:  0.007868,  Adjusted R-squared:  -0.001987 
F-statistic: 0.7983 on 3 and 302 DF,  p-value: 0.4956

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TNFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: TNFA_rank 
Effect size...............: 0.071417 
Standard error............: 0.060266 
Odds ratio (effect size)..: 1.074 
Lower 95% CI..............: 0.954 
Upper 95% CI..............: 1.209 
T-value...................: 1.185028 
P-value...................: 0.2369379 
R^2.......................: 0.007868 
Adjusted r^2..............: -0.001987 
Sample size of AE DB......: 2421 
Sample size of model......: 306 
Missing data %............: 87.3606 

- processing MIF_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
           -0.3051              0.1823  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9832 -0.7831 -0.1190  0.5508  3.0869 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.040947   0.408856   0.100 0.920271    
currentDF[, TRAIT]  0.175121   0.052085   3.362 0.000842 ***
Age                -0.006138   0.005913  -1.038 0.299837    
Gendermale          0.101370   0.110823   0.915 0.360861    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.071 on 431 degrees of freedom
Multiple R-squared:  0.03207,   Adjusted R-squared:  0.02533 
F-statistic:  4.76 on 3 and 431 DF,  p-value: 0.002818

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIF_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: MIF_rank 
Effect size...............: 0.175121 
Standard error............: 0.052085 
Odds ratio (effect size)..: 1.191 
Lower 95% CI..............: 1.076 
Upper 95% CI..............: 1.319 
T-value...................: 3.362233 
P-value...................: 0.0008421348 
R^2.......................: 0.03207 
Adjusted r^2..............: 0.025333 
Sample size of AE DB......: 2421 
Sample size of model......: 435 
Missing data %............: 82.03222 

- processing MCP1_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
           -0.3035              0.2754  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3617 -0.7092 -0.1035  0.5611  3.1254 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.073056   0.403041   0.181    0.856    
currentDF[, TRAIT]  0.267726   0.050486   5.303 1.83e-07 ***
Age                -0.006272   0.005839  -1.074    0.283    
Gendermale          0.069924   0.109679   0.638    0.524    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.049 on 427 degrees of freedom
Multiple R-squared:  0.06926,   Adjusted R-squared:  0.06272 
F-statistic: 10.59 on 3 and 427 DF,  p-value: 9.916e-07

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MCP1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: MCP1_rank 
Effect size...............: 0.267726 
Standard error............: 0.050486 
Odds ratio (effect size)..: 1.307 
Lower 95% CI..............: 1.184 
Upper 95% CI..............: 1.443 
T-value...................: 5.303001 
P-value...................: 1.831903e-07 
R^2.......................: 0.069257 
Adjusted r^2..............: 0.062718 
Sample size of AE DB......: 2421 
Sample size of model......: 431 
Missing data %............: 82.19744 

- processing MIP1a_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age  
          0.329736            0.117434           -0.009516  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1508 -0.7331 -0.1191  0.5654  3.2827 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)  
(Intercept)         0.266367   0.445283   0.598   0.5501  
currentDF[, TRAIT]  0.113586   0.057165   1.987   0.0476 *
Age                -0.009732   0.006457  -1.507   0.1325  
Gendermale          0.112525   0.121253   0.928   0.3540  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.107 on 390 degrees of freedom
Multiple R-squared:  0.01986,   Adjusted R-squared:  0.01232 
F-statistic: 2.634 on 3 and 390 DF,  p-value: 0.04956

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIP1a_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: MIP1a_rank 
Effect size...............: 0.113586 
Standard error............: 0.057165 
Odds ratio (effect size)..: 1.12 
Lower 95% CI..............: 1.002 
Upper 95% CI..............: 1.253 
T-value...................: 1.98698 
P-value...................: 0.04762379 
R^2.......................: 0.019863 
Adjusted r^2..............: 0.012323 
Sample size of AE DB......: 2421 
Sample size of model......: 394 
Missing data %............: 83.72573 

- processing RANTES_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
           -0.3011              0.1694  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9552 -0.7469 -0.1216  0.5535  3.1184 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)   
(Intercept)         0.021100   0.413532   0.051  0.95933   
currentDF[, TRAIT]  0.160303   0.052573   3.049  0.00244 **
Age                -0.005775   0.006000  -0.962  0.33640   
Gendermale          0.098816   0.112897   0.875  0.38192   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.077 on 423 degrees of freedom
Multiple R-squared:  0.02817,   Adjusted R-squared:  0.02128 
F-statistic: 4.087 on 3 and 423 DF,  p-value: 0.007046

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' RANTES_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: RANTES_rank 
Effect size...............: 0.160303 
Standard error............: 0.052573 
Odds ratio (effect size)..: 1.174 
Lower 95% CI..............: 1.059 
Upper 95% CI..............: 1.301 
T-value...................: 3.049156 
P-value...................: 0.002438996 
R^2.......................: 0.028169 
Adjusted r^2..............: 0.021277 
Sample size of AE DB......: 2421 
Sample size of model......: 427 
Missing data %............: 82.36266 

- processing MIG_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)

Coefficients:
(Intercept)          Age  
   0.345456    -0.009483  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0769 -0.7220 -0.0850  0.5347  3.3334 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)         0.299870   0.423296   0.708    0.479
currentDF[, TRAIT] -0.017754   0.054021  -0.329    0.743
Age                -0.009856   0.006131  -1.608    0.109
Gendermale          0.102630   0.114473   0.897    0.370

Residual standard error: 1.091 on 420 degrees of freedom
Multiple R-squared:  0.007796,  Adjusted R-squared:  0.0007085 
F-statistic:   1.1 on 3 and 420 DF,  p-value: 0.3489

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: MIG_rank 
Effect size...............: -0.017754 
Standard error............: 0.054021 
Odds ratio (effect size)..: 0.982 
Lower 95% CI..............: 0.884 
Upper 95% CI..............: 1.092 
T-value...................: -0.328644 
P-value...................: 0.7425884 
R^2.......................: 0.007796 
Adjusted r^2..............: 0.000708 
Sample size of AE DB......: 2421 
Sample size of model......: 424 
Missing data %............: 82.48658 

- processing IP10_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
           -0.3207              0.1701  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0811 -0.7490 -0.0769  0.5478  3.2071 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)   
(Intercept)         0.016843   0.440525   0.038  0.96952   
currentDF[, TRAIT]  0.165313   0.055564   2.975  0.00311 **
Age                -0.006146   0.006411  -0.959  0.33834   
Gendermale          0.114371   0.118226   0.967  0.33396   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.084 on 384 degrees of freedom
Multiple R-squared:  0.02852,   Adjusted R-squared:  0.02093 
F-statistic: 3.758 on 3 and 384 DF,  p-value: 0.01107

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IP10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IP10_rank 
Effect size...............: 0.165313 
Standard error............: 0.055564 
Odds ratio (effect size)..: 1.18 
Lower 95% CI..............: 1.058 
Upper 95% CI..............: 1.316 
T-value...................: 2.975202 
P-value...................: 0.003113081 
R^2.......................: 0.02852 
Adjusted r^2..............: 0.02093 
Sample size of AE DB......: 2421 
Sample size of model......: 388 
Missing data %............: 83.97356 

- processing Eotaxin1_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
    -0.2979  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1174 -0.7320 -0.0801  0.5330  3.3284 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)         0.187312   0.412108   0.455    0.650
currentDF[, TRAIT]  0.024976   0.054611   0.457    0.648
Age                -0.008223   0.005969  -1.378    0.169
Gendermale          0.103651   0.112922   0.918    0.359

Residual standard error: 1.085 on 431 degrees of freedom
Multiple R-squared:  0.007164,  Adjusted R-squared:  0.0002537 
F-statistic: 1.037 on 3 and 431 DF,  p-value: 0.3761

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Eotaxin1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Eotaxin1_rank 
Effect size...............: 0.024976 
Standard error............: 0.054611 
Odds ratio (effect size)..: 1.025 
Lower 95% CI..............: 0.921 
Upper 95% CI..............: 1.141 
T-value...................: 0.457347 
P-value...................: 0.6476522 
R^2.......................: 0.007164 
Adjusted r^2..............: 0.000254 
Sample size of AE DB......: 2421 
Sample size of model......: 435 
Missing data %............: 82.03222 

- processing TARC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
           -0.3658              0.2060  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0965 -0.7023 -0.0993  0.5066  3.2856 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.142221   0.427135   0.333 0.739350    
currentDF[, TRAIT]  0.196073   0.057454   3.413 0.000715 ***
Age                -0.008298   0.006180  -1.343 0.180168    
Gendermale          0.078589   0.117638   0.668 0.504512    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.056 on 369 degrees of freedom
Multiple R-squared:  0.03976,   Adjusted R-squared:  0.03195 
F-statistic: 5.093 on 3 and 369 DF,  p-value: 0.001824

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: TARC_rank 
Effect size...............: 0.196073 
Standard error............: 0.057454 
Odds ratio (effect size)..: 1.217 
Lower 95% CI..............: 1.087 
Upper 95% CI..............: 1.362 
T-value...................: 3.412675 
P-value...................: 0.0007146118 
R^2.......................: 0.03976 
Adjusted r^2..............: 0.031953 
Sample size of AE DB......: 2421 
Sample size of model......: 373 
Missing data %............: 84.59314 

- processing PARC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
           -0.3074              0.2099  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0206 -0.7081 -0.1022  0.5607  2.9481 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.087527   0.405118   0.216    0.829    
currentDF[, TRAIT]  0.207826   0.052435   3.964 8.64e-05 ***
Age                -0.007072   0.005855  -1.208    0.228    
Gendermale          0.121974   0.110295   1.106    0.269    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.066 on 431 degrees of freedom
Multiple R-squared:  0.04161,   Adjusted R-squared:  0.03494 
F-statistic: 6.238 on 3 and 431 DF,  p-value: 0.0003741

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' PARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: PARC_rank 
Effect size...............: 0.207826 
Standard error............: 0.052435 
Odds ratio (effect size)..: 1.231 
Lower 95% CI..............: 1.111 
Upper 95% CI..............: 1.364 
T-value...................: 3.96351 
P-value...................: 8.643532e-05 
R^2.......................: 0.041615 
Adjusted r^2..............: 0.034944 
Sample size of AE DB......: 2421 
Sample size of model......: 435 
Missing data %............: 82.03222 

- processing MDC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age  
          0.314480            0.158713           -0.009263  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1392 -0.7839 -0.0796  0.5834  3.1870 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)   
(Intercept)         0.239102   0.442273   0.541  0.58908   
currentDF[, TRAIT]  0.160688   0.056564   2.841  0.00474 **
Age                -0.009502   0.006410  -1.482  0.13906   
Gendermale          0.133043   0.120548   1.104  0.27043   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.103 on 387 degrees of freedom
Multiple R-squared:  0.03083,   Adjusted R-squared:  0.02332 
F-statistic: 4.103 on 3 and 387 DF,  p-value: 0.006937

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MDC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: MDC_rank 
Effect size...............: 0.160688 
Standard error............: 0.056564 
Odds ratio (effect size)..: 1.174 
Lower 95% CI..............: 1.051 
Upper 95% CI..............: 1.312 
T-value...................: 2.840822 
P-value...................: 0.004737585 
R^2.......................: 0.030829 
Adjusted r^2..............: 0.023316 
Sample size of AE DB......: 2421 
Sample size of model......: 391 
Missing data %............: 83.84965 

- processing OPG_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
           -0.3001              0.1712  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9243 -0.7253 -0.0788  0.5495  3.1685 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)   
(Intercept)         0.127052   0.408695   0.311  0.75605   
currentDF[, TRAIT]  0.163866   0.051508   3.181  0.00157 **
Age                -0.007145   0.005926  -1.206  0.22856   
Gendermale          0.081692   0.111745   0.731  0.46514   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.074 on 430 degrees of freedom
Multiple R-squared:  0.02958,   Adjusted R-squared:  0.02281 
F-statistic: 4.369 on 3 and 430 DF,  p-value: 0.004796

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' OPG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: OPG_rank 
Effect size...............: 0.163866 
Standard error............: 0.051508 
Odds ratio (effect size)..: 1.178 
Lower 95% CI..............: 1.065 
Upper 95% CI..............: 1.303 
T-value...................: 3.181357 
P-value...................: 0.001572083 
R^2.......................: 0.029583 
Adjusted r^2..............: 0.022812 
Sample size of AE DB......: 2421 
Sample size of model......: 434 
Missing data %............: 82.07352 

- processing sICAM1_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
           -0.3014              0.1592  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0829 -0.7520 -0.0844  0.5232  3.1590 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)   
(Intercept)         0.007216   0.412317   0.018  0.98605   
currentDF[, TRAIT]  0.152689   0.051253   2.979  0.00305 **
Age                -0.005724   0.005960  -0.960  0.33741   
Gendermale          0.114929   0.111118   1.034  0.30158   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.074 on 431 degrees of freedom
Multiple R-squared:  0.02672,   Adjusted R-squared:  0.01995 
F-statistic: 3.945 on 3 and 431 DF,  p-value: 0.00853

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' sICAM1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: sICAM1_rank 
Effect size...............: 0.152689 
Standard error............: 0.051253 
Odds ratio (effect size)..: 1.165 
Lower 95% CI..............: 1.054 
Upper 95% CI..............: 1.288 
T-value...................: 2.979145 
P-value...................: 0.003053889 
R^2.......................: 0.026725 
Adjusted r^2..............: 0.01995 
Sample size of AE DB......: 2421 
Sample size of model......: 435 
Missing data %............: 82.03222 

- processing VEGFA_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
     -0.349  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0509 -0.7061 -0.0870  0.5261  3.4293 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)         0.068883   0.439439   0.157    0.876
currentDF[, TRAIT]  0.004020   0.055126   0.073    0.942
Age                -0.007853   0.006297  -1.247    0.213
Gendermale          0.162015   0.121468   1.334    0.183

Residual standard error: 1.06 on 364 degrees of freedom
Multiple R-squared:  0.009134,  Adjusted R-squared:  0.0009677 
F-statistic: 1.118 on 3 and 364 DF,  p-value: 0.3415

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VEGFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: VEGFA_rank 
Effect size...............: 0.00402 
Standard error............: 0.055126 
Odds ratio (effect size)..: 1.004 
Lower 95% CI..............: 0.901 
Upper 95% CI..............: 1.119 
T-value...................: 0.072928 
P-value...................: 0.9419038 
R^2.......................: 0.009134 
Adjusted r^2..............: 0.000968 
Sample size of AE DB......: 2421 
Sample size of model......: 368 
Missing data %............: 84.79967 

- processing TGFB_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
           -0.2787              0.1345  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9736 -0.7129 -0.1016  0.4965  3.1937 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)  
(Intercept)         0.157291   0.416652   0.378    0.706  
currentDF[, TRAIT]  0.132473   0.053085   2.495    0.013 *
Age                -0.007760   0.006024  -1.288    0.198  
Gendermale          0.127319   0.114857   1.108    0.268  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.09 on 421 degrees of freedom
Multiple R-squared:  0.02161,   Adjusted R-squared:  0.01464 
F-statistic: 3.099 on 3 and 421 DF,  p-value: 0.02666

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TGFB_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: TGFB_rank 
Effect size...............: 0.132473 
Standard error............: 0.053085 
Odds ratio (effect size)..: 1.142 
Lower 95% CI..............: 1.029 
Upper 95% CI..............: 1.267 
T-value...................: 2.495473 
P-value...................: 0.01296082 
R^2.......................: 0.021608 
Adjusted r^2..............: 0.014636 
Sample size of AE DB......: 2421 
Sample size of model......: 425 
Missing data %............: 82.44527 

- processing MMP2_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale  
           -0.4434              0.1667              0.1959  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2756 -0.7509 -0.0728  0.5125  3.2664 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)   
(Intercept)         0.022016   0.417955   0.053  0.95801   
currentDF[, TRAIT]  0.160516   0.054876   2.925  0.00363 **
Age                -0.006893   0.006028  -1.143  0.25353   
Gendermale          0.195535   0.114344   1.710  0.08798 . 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.091 on 426 degrees of freedom
Multiple R-squared:  0.02847,   Adjusted R-squared:  0.02163 
F-statistic: 4.162 on 3 and 426 DF,  p-value: 0.006362

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: MMP2_rank 
Effect size...............: 0.160516 
Standard error............: 0.054876 
Odds ratio (effect size)..: 1.174 
Lower 95% CI..............: 1.054 
Upper 95% CI..............: 1.307 
T-value...................: 2.925092 
P-value...................: 0.003627529 
R^2.......................: 0.028474 
Adjusted r^2..............: 0.021633 
Sample size of AE DB......: 2421 
Sample size of model......: 430 
Missing data %............: 82.23874 

- processing MMP8_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
           -0.3044              0.1515  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2735 -0.7497 -0.0894  0.5297  3.1583 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)   
(Intercept)         0.197589   0.415893   0.475   0.6350   
currentDF[, TRAIT]  0.142794   0.054886   2.602   0.0096 **
Age                -0.008529   0.006012  -1.419   0.1567   
Gendermale          0.106678   0.115401   0.924   0.3558   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.093 on 426 degrees of freedom
Multiple R-squared:  0.02446,   Adjusted R-squared:  0.01759 
F-statistic: 3.561 on 3 and 426 DF,  p-value: 0.01434

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: MMP8_rank 
Effect size...............: 0.142794 
Standard error............: 0.054886 
Odds ratio (effect size)..: 1.153 
Lower 95% CI..............: 1.036 
Upper 95% CI..............: 1.285 
T-value...................: 2.601622 
P-value...................: 0.009601555 
R^2.......................: 0.024461 
Adjusted r^2..............: 0.017591 
Sample size of AE DB......: 2421 
Sample size of model......: 430 
Missing data %............: 82.23874 

- processing MMP9_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age  
          0.289977            0.145540           -0.008894  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2652 -0.7691 -0.0965  0.5318  3.2400 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)   
(Intercept)         0.203538   0.415830   0.489  0.62476   
currentDF[, TRAIT]  0.140326   0.052965   2.649  0.00836 **
Age                -0.008940   0.006011  -1.487  0.13767   
Gendermale          0.130474   0.114190   1.143  0.25385   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.093 on 426 degrees of freedom
Multiple R-squared:  0.02503,   Adjusted R-squared:  0.01816 
F-statistic: 3.645 on 3 and 426 DF,  p-value: 0.0128

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: MMP9_rank 
Effect size...............: 0.140326 
Standard error............: 0.052965 
Odds ratio (effect size)..: 1.151 
Lower 95% CI..............: 1.037 
Upper 95% CI..............: 1.277 
T-value...................: 2.649437 
P-value...................: 0.008362477 
R^2.......................: 0.025027 
Adjusted r^2..............: 0.018161 
Sample size of AE DB......: 2421 
Sample size of model......: 430 
Missing data %............: 82.23874 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
DT::datatable(GLM.results)


# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 2

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines as a function of serum/plaque MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of MCP1_pg_ug_2015_rank.

- processing IL2_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ SmokerStatus + Med.Statin.LLD + 
    MedHx_CVD + stenose, data = currentDF)

Coefficients:
             (Intercept)     SmokerStatusEx-smoker  SmokerStatusNever smoked         Med.Statin.LLDyes              MedHx_CVDyes             stenose50-70%             stenose70-90%  
                 -1.4116                   -0.1971                    0.2067                   -0.3136                    0.1780                    0.8013                    1.5344  
           stenose90-99%   stenose100% (Occlusion)  
                  1.4295                    0.8031  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8738 -0.6736 -0.1345  0.4803  3.1937 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -0.9040372  1.3804366  -0.655   0.5130  
currentDF[, TRAIT]         0.0609237  0.0611933   0.996   0.3203  
Age                       -0.0008281  0.0076152  -0.109   0.9135  
Gendermale                 0.1152108  0.1322087   0.871   0.3842  
Hypertension.compositeyes -0.1944219  0.1815830  -1.071   0.2852  
DiabetesStatusDiabetes     0.0484443  0.1521821   0.318   0.7505  
SmokerStatusEx-smoker     -0.1935160  0.1347748  -1.436   0.1521  
SmokerStatusNever smoked   0.2799564  0.2107724   1.328   0.1851  
Med.Statin.LLDyes         -0.2813017  0.1345181  -2.091   0.0374 *
Med.all.antiplateletyes   -0.1563247  0.2226827  -0.702   0.4832  
GFR_MDRD                   0.0003522  0.0034326   0.103   0.9183  
BMI                       -0.0059270  0.0181250  -0.327   0.7439  
MedHx_CVDyes               0.1882475  0.1253976   1.501   0.1344  
stenose50-70%              0.5857424  1.0938979   0.535   0.5927  
stenose70-90%              1.3935845  1.0421384   1.337   0.1822  
stenose90-99%              1.3042041  1.0403354   1.254   0.2110  
stenose100% (Occlusion)    0.4560656  1.2264521   0.372   0.7103  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.027 on 296 degrees of freedom
Multiple R-squared:  0.07597,   Adjusted R-squared:  0.02602 
F-statistic: 1.521 on 16 and 296 DF,  p-value: 0.09096

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL2_rank 
Effect size...............: 0.060924 
Standard error............: 0.061193 
Odds ratio (effect size)..: 1.063 
Lower 95% CI..............: 0.943 
Upper 95% CI..............: 1.198 
T-value...................: 0.995594 
P-value...................: 0.3202605 
R^2.......................: 0.075969 
Adjusted r^2..............: 0.026022 
Sample size of AE DB......: 2421 
Sample size of model......: 313 
Missing data %............: 87.07146 

- processing IL4_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ SmokerStatus + Med.Statin.LLD + 
    MedHx_CVD + stenose, data = currentDF)

Coefficients:
             (Intercept)     SmokerStatusEx-smoker  SmokerStatusNever smoked         Med.Statin.LLDyes              MedHx_CVDyes             stenose70-90%             stenose90-99%  
                -0.59636                  -0.21519                   0.32292                  -0.35784                   0.19828                   0.77301                   0.68296  
 stenose100% (Occlusion)  
                -0.03684  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0207 -0.6799 -0.1317  0.5013  3.1727 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -0.2148910  0.9394517  -0.229   0.8192  
currentDF[, TRAIT]         0.0131585  0.0636832   0.207   0.8365  
Age                        0.0001100  0.0078523   0.014   0.9888  
Gendermale                 0.1170079  0.1382040   0.847   0.3979  
Hypertension.compositeyes -0.2447199  0.1892615  -1.293   0.1971  
DiabetesStatusDiabetes     0.0277828  0.1553730   0.179   0.8582  
SmokerStatusEx-smoker     -0.1987040  0.1361226  -1.460   0.1455  
SmokerStatusNever smoked   0.4070975  0.2229495   1.826   0.0690 .
Med.Statin.LLDyes         -0.3334599  0.1394504  -2.391   0.0175 *
Med.all.antiplateletyes   -0.1590064  0.2334238  -0.681   0.4963  
GFR_MDRD                   0.0007895  0.0037210   0.212   0.8321  
BMI                       -0.0109164  0.0185297  -0.589   0.5563  
MedHx_CVDyes               0.2036860  0.1294762   1.573   0.1168  
stenose70-90%              0.8402572  0.3465787   2.424   0.0160 *
stenose90-99%              0.7757418  0.3408006   2.276   0.0236 *
stenose100% (Occlusion)   -0.2042458  0.7156606  -0.285   0.7756  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.014 on 272 degrees of freedom
Multiple R-squared:  0.08574,   Adjusted R-squared:  0.03532 
F-statistic: 1.701 on 15 and 272 DF,  p-value: 0.05052

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL4_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL4_rank 
Effect size...............: 0.013159 
Standard error............: 0.063683 
Odds ratio (effect size)..: 1.013 
Lower 95% CI..............: 0.894 
Upper 95% CI..............: 1.148 
T-value...................: 0.206624 
P-value...................: 0.8364577 
R^2.......................: 0.08574 
Adjusted r^2..............: 0.035321 
Sample size of AE DB......: 2421 
Sample size of model......: 288 
Missing data %............: 88.10409 

- processing IL5_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ SmokerStatus + Med.Statin.LLD + 
    MedHx_CVD, data = currentDF)

Coefficients:
             (Intercept)     SmokerStatusEx-smoker  SmokerStatusNever smoked         Med.Statin.LLDyes              MedHx_CVDyes  
                 0.04725                  -0.18936                   0.25675                  -0.33530                   0.21401  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0486 -0.6399 -0.1424  0.4628  3.1633 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.134e-01  8.693e-01  -0.130   0.8963  
currentDF[, TRAIT]         2.441e-02  5.874e-02   0.416   0.6780  
Age                       -6.869e-04  7.485e-03  -0.092   0.9269  
Gendermale                 1.257e-01  1.293e-01   0.972   0.3318  
Hypertension.compositeyes -2.627e-01  1.759e-01  -1.493   0.1365  
DiabetesStatusDiabetes    -6.447e-02  1.501e-01  -0.429   0.6679  
SmokerStatusEx-smoker     -1.839e-01  1.301e-01  -1.414   0.1585  
SmokerStatusNever smoked   3.254e-01  2.152e-01   1.512   0.1315  
Med.Statin.LLDyes         -3.144e-01  1.313e-01  -2.395   0.0173 *
Med.all.antiplateletyes   -1.660e-01  2.239e-01  -0.742   0.4590  
GFR_MDRD                   5.308e-05  3.421e-03   0.016   0.9876  
BMI                       -4.430e-04  1.725e-02  -0.026   0.9795  
MedHx_CVDyes               2.150e-01  1.221e-01   1.761   0.0792 .
stenose70-90%              5.506e-01  3.364e-01   1.637   0.1028  
stenose90-99%              5.046e-01  3.333e-01   1.514   0.1312  
stenose100% (Occlusion)   -4.019e-01  7.018e-01  -0.573   0.5673  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9959 on 291 degrees of freedom
Multiple R-squared:  0.07753,   Adjusted R-squared:  0.02998 
F-statistic: 1.631 on 15 and 291 DF,  p-value: 0.06503

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL5_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL5_rank 
Effect size...............: 0.024409 
Standard error............: 0.058738 
Odds ratio (effect size)..: 1.025 
Lower 95% CI..............: 0.913 
Upper 95% CI..............: 1.15 
T-value...................: 0.415556 
P-value...................: 0.6780413 
R^2.......................: 0.077534 
Adjusted r^2..............: 0.029984 
Sample size of AE DB......: 2421 
Sample size of model......: 307 
Missing data %............: 87.31929 

- processing IL6_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ SmokerStatus + Med.Statin.LLD + 
    MedHx_CVD, data = currentDF)

Coefficients:
             (Intercept)     SmokerStatusEx-smoker  SmokerStatusNever smoked         Med.Statin.LLDyes              MedHx_CVDyes  
                 0.02765                  -0.19701                   0.17715                  -0.34026                   0.18317  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9340 -0.7269 -0.1402  0.4952  3.2485 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -0.7304495  1.2039040  -0.607   0.5445  
currentDF[, TRAIT]         0.0495860  0.0605667   0.819   0.4136  
Age                        0.0029375  0.0081022   0.363   0.7172  
Gendermale                 0.0972441  0.1360836   0.715   0.4754  
Hypertension.compositeyes -0.1933556  0.1889107  -1.024   0.3069  
DiabetesStatusDiabetes     0.0314512  0.1578936   0.199   0.8422  
SmokerStatusEx-smoker     -0.2041176  0.1380075  -1.479   0.1402  
SmokerStatusNever smoked   0.2192621  0.2208572   0.993   0.3216  
Med.Statin.LLDyes         -0.3213688  0.1375459  -2.336   0.0201 *
Med.all.antiplateletyes   -0.0103473  0.2179580  -0.047   0.9622  
GFR_MDRD                   0.0002681  0.0036520   0.073   0.9415  
BMI                       -0.0029085  0.0173356  -0.168   0.8669  
MedHx_CVDyes               0.1939384  0.1280896   1.514   0.1311  
stenose50-70%              0.0441398  0.8270848   0.053   0.9575  
stenose70-90%              0.8164480  0.7683489   1.063   0.2888  
stenose90-99%              0.6929481  0.7643636   0.907   0.3654  
stenose100% (Occlusion)    0.0266923  1.0187181   0.026   0.9791  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.063 on 299 degrees of freedom
Multiple R-squared:  0.06977,   Adjusted R-squared:  0.01999 
F-statistic: 1.402 on 16 and 299 DF,  p-value: 0.1392

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL6_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL6_rank 
Effect size...............: 0.049586 
Standard error............: 0.060567 
Odds ratio (effect size)..: 1.051 
Lower 95% CI..............: 0.933 
Upper 95% CI..............: 1.183 
T-value...................: 0.8187 
P-value...................: 0.4136097 
R^2.......................: 0.069768 
Adjusted r^2..............: 0.01999 
Sample size of AE DB......: 2421 
Sample size of model......: 316 
Missing data %............: 86.94754 

- processing IL8_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age   Med.Statin.LLDyes            GFR_MDRD  
          1.319866            0.150243           -0.012446           -0.331128           -0.006835  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1660 -0.6878 -0.0854  0.4392  2.7519 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)  
(Intercept)                0.576633   1.204565   0.479   0.6325  
currentDF[, TRAIT]         0.122587   0.062436   1.963   0.0506 .
Age                       -0.011736   0.008068  -1.455   0.1468  
Gendermale                 0.156818   0.138854   1.129   0.2597  
Hypertension.compositeyes -0.147644   0.191180  -0.772   0.4406  
DiabetesStatusDiabetes     0.071065   0.159455   0.446   0.6562  
SmokerStatusEx-smoker     -0.089324   0.137683  -0.649   0.5170  
SmokerStatusNever smoked   0.219589   0.229128   0.958   0.3387  
Med.Statin.LLDyes         -0.344245   0.136722  -2.518   0.0123 *
Med.all.antiplateletyes    0.109546   0.220699   0.496   0.6200  
GFR_MDRD                  -0.005958   0.003488  -1.708   0.0887 .
BMI                       -0.006302   0.017262  -0.365   0.7153  
MedHx_CVDyes               0.136688   0.129745   1.054   0.2930  
stenose50-70%             -0.113009   0.831479  -0.136   0.8920  
stenose70-90%              0.721268   0.764489   0.943   0.3462  
stenose90-99%              0.663030   0.760440   0.872   0.3840  
stenose100% (Occlusion)    0.153064   0.994391   0.154   0.8778  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.059 on 290 degrees of freedom
Multiple R-squared:  0.08298,   Adjusted R-squared:  0.03239 
F-statistic:  1.64 on 16 and 290 DF,  p-value: 0.05807

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL8_rank 
Effect size...............: 0.122587 
Standard error............: 0.062436 
Odds ratio (effect size)..: 1.13 
Lower 95% CI..............: 1 
Upper 95% CI..............: 1.278 
T-value...................: 1.963394 
P-value...................: 0.05055575 
R^2.......................: 0.082985 
Adjusted r^2..............: 0.032391 
Sample size of AE DB......: 2421 
Sample size of model......: 307 
Missing data %............: 87.31929 

- processing IL9_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age   Med.Statin.LLDyes            GFR_MDRD  
          1.256361            0.120196           -0.012202           -0.369097           -0.006247  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9999 -0.7439 -0.1274  0.5031  3.3027 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                1.309953   1.161403   1.128  0.26018   
currentDF[, TRAIT]         0.114667   0.061271   1.871  0.06217 . 
Age                       -0.012838   0.007711  -1.665  0.09688 . 
Gendermale                 0.120691   0.133653   0.903  0.36718   
Hypertension.compositeyes -0.166469   0.184521  -0.902  0.36763   
DiabetesStatusDiabetes     0.189497   0.157530   1.203  0.22987   
SmokerStatusEx-smoker     -0.050438   0.136801  -0.369  0.71259   
SmokerStatusNever smoked   0.169135   0.202769   0.834  0.40482   
Med.Statin.LLDyes         -0.390577   0.139332  -2.803  0.00536 **
Med.all.antiplateletyes   -0.099250   0.224735  -0.442  0.65905   
GFR_MDRD                  -0.005385   0.003344  -1.610  0.10831   
BMI                       -0.016522   0.016479  -1.003  0.31680   
MedHx_CVDyes               0.142001   0.127331   1.115  0.26558   
stenose50-70%             -0.052841   0.853975  -0.062  0.95070   
stenose70-90%              0.458094   0.795729   0.576  0.56522   
stenose90-99%              0.420588   0.791481   0.531  0.59550   
stenose100% (Occlusion)   -0.302480   0.983396  -0.308  0.75859   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.103 on 329 degrees of freedom
Multiple R-squared:  0.07311,   Adjusted R-squared:  0.02803 
F-statistic: 1.622 on 16 and 329 DF,  p-value: 0.06145

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL9_rank 
Effect size...............: 0.114667 
Standard error............: 0.061271 
Odds ratio (effect size)..: 1.121 
Lower 95% CI..............: 0.995 
Upper 95% CI..............: 1.265 
T-value...................: 1.871456 
P-value...................: 0.0621692 
R^2.......................: 0.073105 
Adjusted r^2..............: 0.028029 
Sample size of AE DB......: 2421 
Sample size of model......: 346 
Missing data %............: 85.70839 

- processing IL10_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ SmokerStatus + Med.Statin.LLD, 
    data = currentDF)

Coefficients:
             (Intercept)     SmokerStatusEx-smoker  SmokerStatusNever smoked         Med.Statin.LLDyes  
                  0.2062                   -0.2315                    0.2807                   -0.3448  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9449 -0.6591 -0.1329  0.5218  3.1719 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.260e-01  9.598e-01  -0.131   0.8956  
currentDF[, TRAIT]         6.375e-02  6.650e-02   0.959   0.3387  
Age                        1.083e-05  8.144e-03   0.001   0.9989  
Gendermale                 2.001e-01  1.420e-01   1.410   0.1598  
Hypertension.compositeyes -2.658e-01  1.971e-01  -1.349   0.1787  
DiabetesStatusDiabetes    -2.252e-03  1.627e-01  -0.014   0.9890  
SmokerStatusEx-smoker     -2.216e-01  1.410e-01  -1.572   0.1172  
SmokerStatusNever smoked   3.889e-01  2.292e-01   1.697   0.0909 .
Med.Statin.LLDyes         -3.198e-01  1.425e-01  -2.244   0.0257 *
Med.all.antiplateletyes   -1.847e-01  2.418e-01  -0.764   0.4456  
GFR_MDRD                   1.158e-05  3.831e-03   0.003   0.9976  
BMI                       -5.242e-03  1.863e-02  -0.281   0.7786  
MedHx_CVDyes               1.426e-01  1.364e-01   1.046   0.2967  
stenose70-90%              6.467e-01  3.686e-01   1.755   0.0805 .
stenose90-99%              6.346e-01  3.634e-01   1.746   0.0820 .
stenose100% (Occlusion)   -3.388e-01  7.351e-01  -0.461   0.6453  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.024 on 259 degrees of freedom
Multiple R-squared:  0.08837,   Adjusted R-squared:  0.03558 
F-statistic: 1.674 on 15 and 259 DF,  p-value: 0.05621

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL10_rank 
Effect size...............: 0.063746 
Standard error............: 0.066499 
Odds ratio (effect size)..: 1.066 
Lower 95% CI..............: 0.936 
Upper 95% CI..............: 1.214 
T-value...................: 0.958602 
P-value...................: 0.3386533 
R^2.......................: 0.088373 
Adjusted r^2..............: 0.035576 
Sample size of AE DB......: 2421 
Sample size of model......: 275 
Missing data %............: 88.64106 

- processing IL12_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Hypertension.composite + 
    SmokerStatus + Med.Statin.LLD + MedHx_CVD + stenose, data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes      SmokerStatusEx-smoker   SmokerStatusNever smoked          Med.Statin.LLDyes               MedHx_CVDyes              stenose70-90%  
                  -0.4426                    -0.3246                    -0.1677                     0.3369                    -0.2917                     0.1949                     0.7764  
            stenose90-99%    stenose100% (Occlusion)  
                   0.7078                     0.1502  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9065 -0.6774 -0.1486  0.5077  3.2004 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -0.421304   0.949003  -0.444   0.6574  
currentDF[, TRAIT]         0.072580   0.064218   1.130   0.2594  
Age                        0.002493   0.008061   0.309   0.7573  
Gendermale                 0.139809   0.137259   1.019   0.3093  
Hypertension.compositeyes -0.355594   0.192595  -1.846   0.0659 .
DiabetesStatusDiabetes     0.036125   0.157758   0.229   0.8190  
SmokerStatusEx-smoker     -0.198593   0.140640  -1.412   0.1591  
SmokerStatusNever smoked   0.341714   0.216600   1.578   0.1158  
Med.Statin.LLDyes         -0.259660   0.140067  -1.854   0.0648 .
Med.all.antiplateletyes   -0.064441   0.234396  -0.275   0.7836  
GFR_MDRD                  -0.000507   0.003728  -0.136   0.8919  
BMI                       -0.007681   0.018628  -0.412   0.6804  
MedHx_CVDyes               0.190034   0.131533   1.445   0.1497  
stenose70-90%              0.803795   0.347726   2.312   0.0216 *
stenose90-99%              0.737758   0.342741   2.153   0.0322 *
stenose100% (Occlusion)    0.139376   0.840365   0.166   0.8684  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.017 on 271 degrees of freedom
Multiple R-squared:  0.08055,   Adjusted R-squared:  0.02966 
F-statistic: 1.583 on 15 and 271 DF,  p-value: 0.07802

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL12_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL12_rank 
Effect size...............: 0.07258 
Standard error............: 0.064218 
Odds ratio (effect size)..: 1.075 
Lower 95% CI..............: 0.948 
Upper 95% CI..............: 1.22 
T-value...................: 1.130204 
P-value...................: 0.2593895 
R^2.......................: 0.08055 
Adjusted r^2..............: 0.029658 
Sample size of AE DB......: 2421 
Sample size of model......: 287 
Missing data %............: 88.14539 

- processing IL13_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age   Med.Statin.LLDyes            GFR_MDRD  
          0.964503            0.118751           -0.009798           -0.320600           -0.004803  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0126 -0.7298 -0.1381  0.4686  3.2438 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                0.988198   1.094849   0.903  0.36732   
currentDF[, TRAIT]         0.103312   0.056624   1.825  0.06887 . 
Age                       -0.010663   0.006996  -1.524  0.12832   
Gendermale                 0.104613   0.121231   0.863  0.38873   
Hypertension.compositeyes -0.064382   0.164812  -0.391  0.69629   
DiabetesStatusDiabetes     0.074290   0.138155   0.538  0.59108   
SmokerStatusEx-smoker     -0.073435   0.123146  -0.596  0.55132   
SmokerStatusNever smoked   0.151039   0.188865   0.800  0.42438   
Med.Statin.LLDyes         -0.338706   0.126468  -2.678  0.00773 **
Med.all.antiplateletyes   -0.118065   0.194270  -0.608  0.54373   
GFR_MDRD                  -0.004081   0.003105  -1.314  0.18958   
BMI                       -0.016222   0.015022  -1.080  0.28089   
MedHx_CVDyes               0.123395   0.114855   1.074  0.28336   
stenose50-70%             -0.078735   0.821047  -0.096  0.92365   
stenose70-90%              0.524287   0.771531   0.680  0.49721   
stenose90-99%              0.435535   0.768951   0.566  0.57146   
stenose100% (Occlusion)   -0.282454   0.950979  -0.297  0.76662   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.071 on 376 degrees of freedom
Multiple R-squared:  0.06264,   Adjusted R-squared:  0.02275 
F-statistic:  1.57 on 16 and 376 DF,  p-value: 0.07396

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL13_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL13_rank 
Effect size...............: 0.103312 
Standard error............: 0.056624 
Odds ratio (effect size)..: 1.109 
Lower 95% CI..............: 0.992 
Upper 95% CI..............: 1.239 
T-value...................: 1.82453 
P-value...................: 0.06886544 
R^2.......................: 0.06264 
Adjusted r^2..............: 0.022753 
Sample size of AE DB......: 2421 
Sample size of model......: 393 
Missing data %............: 83.76704 

- processing IL21_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age   Med.Statin.LLDyes            GFR_MDRD  
          0.977989            0.117809           -0.010001           -0.319791           -0.004799  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0032 -0.7404 -0.1423  0.4567  3.2706 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                1.031232   1.093286   0.943  0.34616   
currentDF[, TRAIT]         0.102924   0.057311   1.796  0.07331 . 
Age                       -0.010760   0.006959  -1.546  0.12286   
Gendermale                 0.098167   0.120932   0.812  0.41745   
Hypertension.compositeyes -0.070588   0.164409  -0.429  0.66792   
DiabetesStatusDiabetes     0.067436   0.137810   0.489  0.62488   
SmokerStatusEx-smoker     -0.068904   0.122721  -0.561  0.57481   
SmokerStatusNever smoked   0.152576   0.188483   0.809  0.41874   
Med.Statin.LLDyes         -0.340013   0.125994  -2.699  0.00728 **
Med.all.antiplateletyes   -0.115920   0.193994  -0.598  0.55050   
GFR_MDRD                  -0.004082   0.003101  -1.316  0.18893   
BMI                       -0.016690   0.014994  -1.113  0.26637   
MedHx_CVDyes               0.123932   0.114391   1.083  0.27932   
stenose50-70%             -0.100223   0.820921  -0.122  0.90290   
stenose70-90%              0.509454   0.771305   0.661  0.50933   
stenose90-99%              0.420669   0.768857   0.547  0.58461   
stenose100% (Occlusion)   -0.320906   0.950217  -0.338  0.73576   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.07 on 377 degrees of freedom
Multiple R-squared:  0.06236,   Adjusted R-squared:  0.02256 
F-statistic: 1.567 on 16 and 377 DF,  p-value: 0.07492

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL21_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IL21_rank 
Effect size...............: 0.102924 
Standard error............: 0.057311 
Odds ratio (effect size)..: 1.108 
Lower 95% CI..............: 0.991 
Upper 95% CI..............: 1.24 
T-value...................: 1.795899 
P-value...................: 0.07331124 
R^2.......................: 0.062357 
Adjusted r^2..............: 0.022563 
Sample size of AE DB......: 2421 
Sample size of model......: 394 
Missing data %............: 83.72573 

- processing INFG_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    SmokerStatus + Med.Statin.LLD, data = currentDF)

Coefficients:
             (Intercept)        currentDF[, TRAIT]                Gendermale     SmokerStatusEx-smoker  SmokerStatusNever smoked         Med.Statin.LLDyes  
                -0.04532                   0.13160                   0.21032                  -0.20798                   0.17441                  -0.27012  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8780 -0.7011 -0.1219  0.5258  3.2096 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)  
(Intercept)                0.345506   1.176686   0.294   0.7693  
currentDF[, TRAIT]         0.129547   0.068670   1.887   0.0602 .
Age                       -0.004679   0.007940  -0.589   0.5561  
Gendermale                 0.237954   0.138562   1.717   0.0870 .
Hypertension.compositeyes -0.258138   0.191356  -1.349   0.1784  
DiabetesStatusDiabetes     0.002097   0.155583   0.013   0.9893  
SmokerStatusEx-smoker     -0.185635   0.137205  -1.353   0.1771  
SmokerStatusNever smoked   0.274468   0.221640   1.238   0.2166  
Med.Statin.LLDyes         -0.285792   0.142507  -2.005   0.0458 *
Med.all.antiplateletyes   -0.095334   0.217043  -0.439   0.6608  
GFR_MDRD                  -0.002449   0.003548  -0.690   0.4906  
BMI                       -0.005831   0.016991  -0.343   0.7317  
MedHx_CVDyes               0.129744   0.132430   0.980   0.3280  
stenose50-70%             -0.178137   0.839176  -0.212   0.8320  
stenose70-90%              0.530370   0.768914   0.690   0.4909  
stenose90-99%              0.443557   0.764178   0.580   0.5621  
stenose100% (Occlusion)   -0.140367   1.101718  -0.127   0.8987  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.054 on 289 degrees of freedom
Multiple R-squared:  0.07563,   Adjusted R-squared:  0.02445 
F-statistic: 1.478 on 16 and 289 DF,  p-value: 0.1067

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' INFG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: INFG_rank 
Effect size...............: 0.129547 
Standard error............: 0.06867 
Odds ratio (effect size)..: 1.138 
Lower 95% CI..............: 0.995 
Upper 95% CI..............: 1.302 
T-value...................: 1.886511 
P-value...................: 0.06022857 
R^2.......................: 0.075628 
Adjusted r^2..............: 0.024452 
Sample size of AE DB......: 2421 
Sample size of model......: 306 
Missing data %............: 87.3606 

- processing TNFA_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Med.Statin.LLD, data = currentDF)

Coefficients:
      (Intercept)  Med.Statin.LLDyes  
          0.03563           -0.28879  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9968 -0.6898 -0.1119  0.5027  3.1775 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -0.2410441  0.9411949  -0.256   0.7981  
currentDF[, TRAIT]         0.0769762  0.0635463   1.211   0.2269  
Age                       -0.0020374  0.0079428  -0.257   0.7978  
Gendermale                 0.1919003  0.1383534   1.387   0.1666  
Hypertension.compositeyes -0.2743491  0.1911496  -1.435   0.1524  
DiabetesStatusDiabetes    -0.1256775  0.1581838  -0.795   0.4276  
SmokerStatusEx-smoker     -0.1322331  0.1401695  -0.943   0.3464  
SmokerStatusNever smoked   0.3171643  0.2240079   1.416   0.1580  
Med.Statin.LLDyes         -0.2636542  0.1394139  -1.891   0.0597 .
Med.all.antiplateletyes   -0.0168753  0.2383434  -0.071   0.9436  
GFR_MDRD                  -0.0007049  0.0036888  -0.191   0.8486  
BMI                       -0.0067393  0.0187438  -0.360   0.7195  
MedHx_CVDyes               0.1308073  0.1334987   0.980   0.3281  
stenose70-90%              0.7836166  0.3472010   2.257   0.0248 *
stenose90-99%              0.7406320  0.3410943   2.171   0.0308 *
stenose100% (Occlusion)    0.1789049  0.8398722   0.213   0.8315  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.01 on 263 degrees of freedom
Multiple R-squared:  0.06963,   Adjusted R-squared:  0.01657 
F-statistic: 1.312 on 15 and 263 DF,  p-value: 0.1943

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TNFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: TNFA_rank 
Effect size...............: 0.076976 
Standard error............: 0.063546 
Odds ratio (effect size)..: 1.08 
Lower 95% CI..............: 0.954 
Upper 95% CI..............: 1.223 
T-value...................: 1.21134 
P-value...................: 0.2268525 
R^2.......................: 0.06963 
Adjusted r^2..............: 0.016567 
Sample size of AE DB......: 2421 
Sample size of model......: 279 
Missing data %............: 88.47584 

- processing MIF_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]   Med.Statin.LLDyes  
           -0.0688              0.2071             -0.2772  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8087 -0.7132 -0.1529  0.5068  3.2120 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.054350   1.080507   0.976  0.32979    
currentDF[, TRAIT]         0.201269   0.057412   3.506  0.00051 ***
Age                       -0.009577   0.006863  -1.395  0.16371    
Gendermale                 0.093257   0.119079   0.783  0.43403    
Hypertension.compositeyes -0.056140   0.162542  -0.345  0.72999    
DiabetesStatusDiabetes     0.131902   0.137680   0.958  0.33866    
SmokerStatusEx-smoker     -0.004499   0.121789  -0.037  0.97055    
SmokerStatusNever smoked   0.255062   0.185645   1.374  0.17028    
Med.Statin.LLDyes         -0.321880   0.124618  -2.583  0.01017 *  
Med.all.antiplateletyes   -0.164964   0.192404  -0.857  0.39178    
GFR_MDRD                  -0.002749   0.003095  -0.888  0.37495    
BMI                       -0.021075   0.014851  -1.419  0.15667    
MedHx_CVDyes               0.098046   0.113354   0.865  0.38761    
stenose50-70%             -0.156727   0.810407  -0.193  0.84676    
stenose70-90%              0.416527   0.761906   0.547  0.58491    
stenose90-99%              0.303059   0.759791   0.399  0.69021    
stenose100% (Occlusion)   -0.436375   0.939870  -0.464  0.64271    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.057 on 377 degrees of freedom
Multiple R-squared:  0.08419,   Adjusted R-squared:  0.04532 
F-statistic: 2.166 on 16 and 377 DF,  p-value: 0.005844

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIF_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: MIF_rank 
Effect size...............: 0.201269 
Standard error............: 0.057412 
Odds ratio (effect size)..: 1.223 
Lower 95% CI..............: 1.093 
Upper 95% CI..............: 1.369 
T-value...................: 3.505686 
P-value...................: 0.0005102625 
R^2.......................: 0.08419 
Adjusted r^2..............: 0.045322 
Sample size of AE DB......: 2421 
Sample size of model......: 394 
Missing data %............: 83.72573 

- processing MCP1_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age   Med.Statin.LLDyes            GFR_MDRD  
          0.887810            0.271500           -0.009283           -0.264622           -0.004857  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2445 -0.6366 -0.1189  0.5190  2.9163 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                0.649319   1.068337   0.608    0.544    
currentDF[, TRAIT]         0.262656   0.052933   4.962 1.06e-06 ***
Age                       -0.010184   0.006802  -1.497    0.135    
Gendermale                 0.071277   0.118586   0.601    0.548    
Hypertension.compositeyes -0.041172   0.160232  -0.257    0.797    
DiabetesStatusDiabetes     0.145833   0.135771   1.074    0.283    
SmokerStatusEx-smoker     -0.041040   0.119621  -0.343    0.732    
SmokerStatusNever smoked   0.174445   0.182174   0.958    0.339    
Med.Statin.LLDyes         -0.283983   0.123431  -2.301    0.022 *  
Med.all.antiplateletyes   -0.075796   0.193772  -0.391    0.696    
GFR_MDRD                  -0.004053   0.003024  -1.340    0.181    
BMI                       -0.012800   0.014682  -0.872    0.384    
MedHx_CVDyes               0.090957   0.112113   0.811    0.418    
stenose50-70%              0.051541   0.795189   0.065    0.948    
stenose70-90%              0.621010   0.746468   0.832    0.406    
stenose90-99%              0.569344   0.743472   0.766    0.444    
stenose100% (Occlusion)    0.063518   0.924900   0.069    0.945    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.039 on 373 degrees of freedom
Multiple R-squared:  0.1144,    Adjusted R-squared:  0.07638 
F-statistic:  3.01 on 16 and 373 DF,  p-value: 9.262e-05

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MCP1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: MCP1_rank 
Effect size...............: 0.262656 
Standard error............: 0.052933 
Odds ratio (effect size)..: 1.3 
Lower 95% CI..............: 1.172 
Upper 95% CI..............: 1.443 
T-value...................: 4.962062 
P-value...................: 1.062343e-06 
R^2.......................: 0.114367 
Adjusted r^2..............: 0.076378 
Sample size of AE DB......: 2421 
Sample size of model......: 390 
Missing data %............: 83.89095 

- processing MIP1a_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age   Med.Statin.LLDyes            GFR_MDRD  
          1.236175            0.131972           -0.012854           -0.390998           -0.005183  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9837 -0.7244 -0.1444  0.4723  3.3736 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                1.303368   1.137919   1.145  0.25285   
currentDF[, TRAIT]         0.127706   0.060926   2.096  0.03681 * 
Age                       -0.012736   0.007506  -1.697  0.09065 . 
Gendermale                 0.107920   0.131338   0.822  0.41183   
Hypertension.compositeyes -0.159046   0.177538  -0.896  0.37097   
DiabetesStatusDiabetes     0.172815   0.149709   1.154  0.24917   
SmokerStatusEx-smoker     -0.072837   0.132953  -0.548  0.58416   
SmokerStatusNever smoked   0.133005   0.198434   0.670  0.50314   
Med.Statin.LLDyes         -0.408097   0.135686  -3.008  0.00283 **
Med.all.antiplateletyes   -0.081988   0.218015  -0.376  0.70710   
GFR_MDRD                  -0.004444   0.003243  -1.370  0.17155   
BMI                       -0.015436   0.015863  -0.973  0.33121   
MedHx_CVDyes               0.129081   0.124117   1.040  0.29908   
stenose50-70%             -0.135584   0.846966  -0.160  0.87291   
stenose70-90%              0.392779   0.789229   0.498  0.61904   
stenose90-99%              0.334327   0.785771   0.425  0.67076   
stenose100% (Occlusion)   -0.371391   0.973883  -0.381  0.70318   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.092 on 340 degrees of freedom
Multiple R-squared:  0.07594,   Adjusted R-squared:  0.03245 
F-statistic: 1.746 on 16 and 340 DF,  p-value: 0.03725

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIP1a_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: MIP1a_rank 
Effect size...............: 0.127706 
Standard error............: 0.060926 
Odds ratio (effect size)..: 1.136 
Lower 95% CI..............: 1.008 
Upper 95% CI..............: 1.28 
T-value...................: 2.096084 
P-value...................: 0.03681363 
R^2.......................: 0.07594 
Adjusted r^2..............: 0.032454 
Sample size of AE DB......: 2421 
Sample size of model......: 357 
Missing data %............: 85.25403 

- processing RANTES_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age   Med.Statin.LLDyes            GFR_MDRD  
          0.937920            0.139942           -0.009339           -0.303461           -0.005060  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.84013 -0.70001 -0.09812  0.51104  3.12078 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                1.032069   1.093300   0.944  0.34579   
currentDF[, TRAIT]         0.151617   0.055684   2.723  0.00678 **
Age                       -0.010713   0.006973  -1.536  0.12530   
Gendermale                 0.102464   0.122244   0.838  0.40246   
Hypertension.compositeyes -0.119339   0.166402  -0.717  0.47372   
DiabetesStatusDiabetes     0.156773   0.140755   1.114  0.26609   
SmokerStatusEx-smoker     -0.013911   0.123242  -0.113  0.91019   
SmokerStatusNever smoked   0.230694   0.187271   1.232  0.21878   
Med.Statin.LLDyes         -0.329808   0.126887  -2.599  0.00972 **
Med.all.antiplateletyes   -0.050260   0.199197  -0.252  0.80094   
GFR_MDRD                  -0.004449   0.003109  -1.431  0.15336   
BMI                       -0.019782   0.015087  -1.311  0.19061   
MedHx_CVDyes               0.123071   0.115904   1.062  0.28900   
stenose50-70%             -0.116765   0.820489  -0.142  0.88691   
stenose70-90%              0.504987   0.766700   0.659  0.51053   
stenose90-99%              0.455322   0.763210   0.597  0.55115   
stenose100% (Occlusion)   -0.430368   0.950207  -0.453  0.65087   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.065 on 370 degrees of freedom
Multiple R-squared:  0.07536,   Adjusted R-squared:  0.03538 
F-statistic: 1.885 on 16 and 370 DF,  p-value: 0.02057

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' RANTES_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: RANTES_rank 
Effect size...............: 0.151617 
Standard error............: 0.055684 
Odds ratio (effect size)..: 1.164 
Lower 95% CI..............: 1.043 
Upper 95% CI..............: 1.298 
T-value...................: 2.722792 
P-value...................: 0.006779779 
R^2.......................: 0.075365 
Adjusted r^2..............: 0.035381 
Sample size of AE DB......: 2421 
Sample size of model......: 387 
Missing data %............: 84.01487 

- processing MIG_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Med.Statin.LLD + GFR_MDRD, 
    data = currentDF)

Coefficients:
      (Intercept)                Age  Med.Statin.LLDyes           GFR_MDRD  
         1.245666          -0.013019          -0.341365          -0.005425  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9508 -0.7029 -0.1107  0.5033  3.2417 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                1.212729   1.113390   1.089  0.27677   
currentDF[, TRAIT]        -0.010196   0.058376  -0.175  0.86144   
Age                       -0.013985   0.007161  -1.953  0.05159 . 
Gendermale                 0.103879   0.123988   0.838  0.40268   
Hypertension.compositeyes -0.084696   0.168769  -0.502  0.61608   
DiabetesStatusDiabetes     0.100572   0.142223   0.707  0.47993   
SmokerStatusEx-smoker     -0.034110   0.126262  -0.270  0.78720   
SmokerStatusNever smoked   0.158732   0.195464   0.812  0.41728   
Med.Statin.LLDyes         -0.363626   0.128925  -2.820  0.00506 **
Med.all.antiplateletyes   -0.119901   0.199244  -0.602  0.54769   
GFR_MDRD                  -0.004730   0.003151  -1.501  0.13421   
BMI                       -0.016765   0.015269  -1.098  0.27294   
MedHx_CVDyes               0.100026   0.118091   0.847  0.39754   
stenose50-70%             -0.024618   0.833137  -0.030  0.97644   
stenose70-90%              0.604029   0.778402   0.776  0.43826   
stenose90-99%              0.537696   0.775346   0.693  0.48844   
stenose100% (Occlusion)   -0.291927   0.961227  -0.304  0.76153   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.081 on 366 degrees of freedom
Multiple R-squared:  0.05616,   Adjusted R-squared:  0.01489 
F-statistic: 1.361 on 16 and 366 DF,  p-value: 0.1582

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: MIG_rank 
Effect size...............: -0.010196 
Standard error............: 0.058376 
Odds ratio (effect size)..: 0.99 
Lower 95% CI..............: 0.883 
Upper 95% CI..............: 1.11 
T-value...................: -0.174664 
P-value...................: 0.8614404 
R^2.......................: 0.056155 
Adjusted r^2..............: 0.014894 
Sample size of AE DB......: 2421 
Sample size of model......: 383 
Missing data %............: 84.18009 

- processing IP10_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD + 
    MedHx_CVD, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]   Med.Statin.LLDyes        MedHx_CVDyes  
           -0.1740              0.1632             -0.3386              0.2106  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9485 -0.7086 -0.1010  0.5620  3.1718 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                0.834081   1.134311   0.735   0.4627   
currentDF[, TRAIT]         0.162891   0.059370   2.744   0.0064 **
Age                       -0.008809   0.007511  -1.173   0.2417   
Gendermale                 0.116944   0.128085   0.913   0.3619   
Hypertension.compositeyes -0.141101   0.174465  -0.809   0.4192   
DiabetesStatusDiabetes     0.167958   0.148935   1.128   0.2602   
SmokerStatusEx-smoker     -0.095488   0.131837  -0.724   0.4694   
SmokerStatusNever smoked  -0.040579   0.203609  -0.199   0.8421   
Med.Statin.LLDyes         -0.375825   0.133605  -2.813   0.0052 **
Med.all.antiplateletyes   -0.134307   0.203616  -0.660   0.5100   
GFR_MDRD                  -0.005065   0.003257  -1.555   0.1209   
BMI                       -0.010462   0.015801  -0.662   0.5084   
MedHx_CVDyes               0.213841   0.121966   1.753   0.0805 . 
stenose50-70%             -0.078219   0.824424  -0.095   0.9245   
stenose70-90%              0.510502   0.771500   0.662   0.5086   
stenose90-99%              0.410846   0.767642   0.535   0.5929   
stenose100% (Occlusion)   -0.155864   0.952645  -0.164   0.8701   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.07 on 333 degrees of freedom
Multiple R-squared:  0.08354,   Adjusted R-squared:  0.03951 
F-statistic: 1.897 on 16 and 333 DF,  p-value: 0.01987

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IP10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IP10_rank 
Effect size...............: 0.162891 
Standard error............: 0.05937 
Odds ratio (effect size)..: 1.177 
Lower 95% CI..............: 1.048 
Upper 95% CI..............: 1.322 
T-value...................: 2.743673 
P-value...................: 0.00640467 
R^2.......................: 0.083545 
Adjusted r^2..............: 0.039511 
Sample size of AE DB......: 2421 
Sample size of model......: 350 
Missing data %............: 85.54316 

- processing Eotaxin1_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Med.Statin.LLD + GFR_MDRD, 
    data = currentDF)

Coefficients:
      (Intercept)                Age  Med.Statin.LLDyes           GFR_MDRD  
         1.088988          -0.011411          -0.319146          -0.004988  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9955 -0.7281 -0.1309  0.4653  3.2617 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                1.038025   1.097857   0.946  0.34501   
currentDF[, TRAIT]         0.027195   0.058583   0.464  0.64276   
Age                       -0.012122   0.006959  -1.742  0.08232 . 
Gendermale                 0.113409   0.122077   0.929  0.35348   
Hypertension.compositeyes -0.079054   0.164995  -0.479  0.63212   
DiabetesStatusDiabetes     0.060665   0.138308   0.439  0.66118   
SmokerStatusEx-smoker     -0.057817   0.123264  -0.469  0.63930   
SmokerStatusNever smoked   0.181883   0.189737   0.959  0.33837   
Med.Statin.LLDyes         -0.339102   0.126496  -2.681  0.00767 **
Med.all.antiplateletyes   -0.106585   0.194788  -0.547  0.58457   
GFR_MDRD                  -0.004253   0.003115  -1.365  0.17298   
BMI                       -0.017199   0.015055  -1.142  0.25399   
MedHx_CVDyes               0.126692   0.114867   1.103  0.27076   
stenose50-70%             -0.018179   0.824248  -0.022  0.98242   
stenose70-90%              0.595972   0.774390   0.770  0.44202   
stenose90-99%              0.516781   0.771815   0.670  0.50354   
stenose100% (Occlusion)   -0.288566   0.954406  -0.302  0.76255   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.074 on 377 degrees of freedom
Multiple R-squared:  0.05488,   Adjusted R-squared:  0.01476 
F-statistic: 1.368 on 16 and 377 DF,  p-value: 0.1542

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Eotaxin1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Eotaxin1_rank 
Effect size...............: 0.027195 
Standard error............: 0.058583 
Odds ratio (effect size)..: 1.028 
Lower 95% CI..............: 0.916 
Upper 95% CI..............: 1.153 
T-value...................: 0.464221 
P-value...................: 0.6427576 
R^2.......................: 0.054875 
Adjusted r^2..............: 0.014764 
Sample size of AE DB......: 2421 
Sample size of model......: 394 
Missing data %............: 83.72573 

- processing TARC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age   Med.Statin.LLDyes            GFR_MDRD  
          1.230714            0.180738           -0.012472           -0.350318           -0.006449  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9124 -0.6663 -0.0809  0.4529  3.4131 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                1.653635   1.129639   1.464  0.14422   
currentDF[, TRAIT]         0.170766   0.060421   2.826  0.00501 **
Age                       -0.012940   0.007286  -1.776  0.07670 . 
Gendermale                 0.094517   0.127846   0.739  0.46027   
Hypertension.compositeyes -0.198279   0.172386  -1.150  0.25092   
DiabetesStatusDiabetes     0.136319   0.148120   0.920  0.35810   
SmokerStatusEx-smoker     -0.044670   0.130880  -0.341  0.73310   
SmokerStatusNever smoked   0.201623   0.193233   1.043  0.29754   
Med.Statin.LLDyes         -0.368263   0.137363  -2.681  0.00772 **
Med.all.antiplateletyes   -0.060989   0.207130  -0.294  0.76861   
GFR_MDRD                  -0.006674   0.003355  -1.989  0.04753 * 
BMI                       -0.022655   0.016203  -1.398  0.16304   
MedHx_CVDyes               0.080311   0.123200   0.652  0.51495   
stenose50-70%             -0.110835   0.813247  -0.136  0.89168   
stenose70-90%              0.373469   0.758142   0.493  0.62263   
stenose90-99%              0.311653   0.754911   0.413  0.68001   
stenose100% (Occlusion)   -0.361833   0.937631  -0.386  0.69983   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.051 on 319 degrees of freedom
Multiple R-squared:  0.09163,   Adjusted R-squared:  0.04607 
F-statistic: 2.011 on 16 and 319 DF,  p-value: 0.01224

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: TARC_rank 
Effect size...............: 0.170766 
Standard error............: 0.060421 
Odds ratio (effect size)..: 1.186 
Lower 95% CI..............: 1.054 
Upper 95% CI..............: 1.335 
T-value...................: 2.826284 
P-value...................: 0.005006253 
R^2.......................: 0.091634 
Adjusted r^2..............: 0.046073 
Sample size of AE DB......: 2421 
Sample size of model......: 336 
Missing data %............: 86.12144 

- processing PARC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]   Med.Statin.LLDyes  
          -0.07028             0.20862            -0.27610  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8915 -0.7156 -0.1147  0.5200  3.0281 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                0.764232   1.084946   0.704 0.481621    
currentDF[, TRAIT]         0.186416   0.055833   3.339 0.000925 ***
Age                       -0.010738   0.006843  -1.569 0.117435    
Gendermale                 0.122878   0.118973   1.033 0.302346    
Hypertension.compositeyes -0.034904   0.163201  -0.214 0.830762    
DiabetesStatusDiabetes     0.115286   0.137344   0.839 0.401776    
SmokerStatusEx-smoker     -0.039201   0.121239  -0.323 0.746618    
SmokerStatusNever smoked   0.197144   0.185114   1.065 0.287563    
Med.Statin.LLDyes         -0.324616   0.124779  -2.602 0.009647 ** 
Med.all.antiplateletyes   -0.069714   0.192129  -0.363 0.716922    
GFR_MDRD                  -0.003449   0.003078  -1.121 0.263151    
BMI                       -0.018064   0.014837  -1.218 0.224168    
MedHx_CVDyes               0.109304   0.113335   0.964 0.335446    
stenose50-70%              0.120849   0.810850   0.149 0.881602    
stenose70-90%              0.637147   0.760674   0.838 0.402781    
stenose90-99%              0.544139   0.757639   0.718 0.473076    
stenose100% (Occlusion)   -0.047844   0.942407  -0.051 0.959537    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.059 on 377 degrees of freedom
Multiple R-squared:  0.08149,   Adjusted R-squared:  0.04251 
F-statistic: 2.091 on 16 and 377 DF,  p-value: 0.008255

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' PARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: PARC_rank 
Effect size...............: 0.186416 
Standard error............: 0.055833 
Odds ratio (effect size)..: 1.205 
Lower 95% CI..............: 1.08 
Upper 95% CI..............: 1.344 
T-value...................: 3.338792 
P-value...................: 0.0009253009 
R^2.......................: 0.081494 
Adjusted r^2..............: 0.042513 
Sample size of AE DB......: 2421 
Sample size of model......: 394 
Missing data %............: 83.72573 

- processing MDC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age   Med.Statin.LLDyes            GFR_MDRD  
          1.272743            0.158371           -0.013009           -0.399520           -0.005476  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9638 -0.6982 -0.1252  0.5091  3.2758 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                1.457356   1.134077   1.285  0.19965   
currentDF[, TRAIT]         0.159091   0.060418   2.633  0.00885 **
Age                       -0.013851   0.007378  -1.877  0.06134 . 
Gendermale                 0.133867   0.130590   1.025  0.30606   
Hypertension.compositeyes -0.164649   0.178053  -0.925  0.35577   
DiabetesStatusDiabetes     0.214873   0.151813   1.415  0.15788   
SmokerStatusEx-smoker     -0.023411   0.131993  -0.177  0.85933   
SmokerStatusNever smoked   0.160679   0.196933   0.816  0.41513   
Med.Statin.LLDyes         -0.427670   0.134004  -3.191  0.00155 **
Med.all.antiplateletyes   -0.065266   0.221580  -0.295  0.76852   
GFR_MDRD                  -0.004886   0.003275  -1.492  0.13667   
BMI                       -0.017952   0.015874  -1.131  0.25892   
MedHx_CVDyes               0.146186   0.123938   1.180  0.23903   
stenose50-70%             -0.149530   0.843365  -0.177  0.85938   
stenose70-90%              0.362155   0.786393   0.461  0.64544   
stenose90-99%              0.282146   0.783403   0.360  0.71896   
stenose100% (Occlusion)   -0.408052   0.970377  -0.421  0.67438   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.088 on 337 degrees of freedom
Multiple R-squared:  0.087, Adjusted R-squared:  0.04365 
F-statistic: 2.007 on 16 and 337 DF,  p-value: 0.01231

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MDC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: MDC_rank 
Effect size...............: 0.159091 
Standard error............: 0.060418 
Odds ratio (effect size)..: 1.172 
Lower 95% CI..............: 1.042 
Upper 95% CI..............: 1.32 
T-value...................: 2.633155 
P-value...................: 0.008849335 
R^2.......................: 0.087 
Adjusted r^2..............: 0.043653 
Sample size of AE DB......: 2421 
Sample size of model......: 354 
Missing data %............: 85.37794 

- processing OPG_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age   Med.Statin.LLDyes            GFR_MDRD  
          0.998102            0.189516           -0.009877           -0.314467           -0.005257  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7655 -0.6928 -0.1118  0.5148  3.1615 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                0.870125   1.084450   0.802 0.422848    
currentDF[, TRAIT]         0.181413   0.054163   3.349 0.000892 ***
Age                       -0.010456   0.006880  -1.520 0.129378    
Gendermale                 0.082936   0.120175   0.690 0.490542    
Hypertension.compositeyes -0.031125   0.163556  -0.190 0.849176    
DiabetesStatusDiabetes     0.096485   0.136953   0.705 0.481551    
SmokerStatusEx-smoker     -0.084674   0.121769  -0.695 0.487258    
SmokerStatusNever smoked   0.148777   0.185920   0.800 0.424088    
Med.Statin.LLDyes         -0.330477   0.125197  -2.640 0.008645 ** 
Med.all.antiplateletyes   -0.149109   0.192599  -0.774 0.439303    
GFR_MDRD                  -0.004349   0.003070  -1.417 0.157436    
BMI                       -0.014832   0.014874  -0.997 0.319323    
MedHx_CVDyes               0.123154   0.113653   1.084 0.279239    
stenose50-70%              0.037336   0.811202   0.046 0.963315    
stenose70-90%              0.616899   0.761605   0.810 0.418454    
stenose90-99%              0.531979   0.758561   0.701 0.483550    
stenose100% (Occlusion)   -0.063606   0.943218  -0.067 0.946271    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.06 on 376 degrees of freedom
Multiple R-squared:  0.08174,   Adjusted R-squared:  0.04266 
F-statistic: 2.092 on 16 and 376 DF,  p-value: 0.008213

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' OPG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: OPG_rank 
Effect size...............: 0.181413 
Standard error............: 0.054163 
Odds ratio (effect size)..: 1.199 
Lower 95% CI..............: 1.078 
Upper 95% CI..............: 1.333 
T-value...................: 3.349384 
P-value...................: 0.0008918522 
R^2.......................: 0.081739 
Adjusted r^2..............: 0.042664 
Sample size of AE DB......: 2421 
Sample size of model......: 393 
Missing data %............: 83.76704 

- processing sICAM1_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]   Med.Statin.LLDyes  
          -0.07021             0.16636            -0.27053  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9402 -0.6903 -0.1354  0.5022  3.1009 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                0.901026   1.089216   0.827  0.40863   
currentDF[, TRAIT]         0.143573   0.054927   2.614  0.00931 **
Age                       -0.009282   0.006968  -1.332  0.18367   
Gendermale                 0.113172   0.119686   0.946  0.34497   
Hypertension.compositeyes -0.077803   0.163550  -0.476  0.63456   
DiabetesStatusDiabetes     0.096341   0.137810   0.699  0.48493   
SmokerStatusEx-smoker     -0.046386   0.121873  -0.381  0.70371   
SmokerStatusNever smoked   0.178301   0.186253   0.957  0.33903   
Med.Statin.LLDyes         -0.314030   0.125774  -2.497  0.01296 * 
Med.all.antiplateletyes   -0.104653   0.192948  -0.542  0.58787   
GFR_MDRD                  -0.003602   0.003096  -1.163  0.24546   
BMI                       -0.018641   0.014926  -1.249  0.21247   
MedHx_CVDyes               0.099486   0.114356   0.870  0.38488   
stenose50-70%             -0.090352   0.815647  -0.111  0.91185   
stenose70-90%              0.519829   0.766017   0.679  0.49780   
stenose90-99%              0.426714   0.763337   0.559  0.57649   
stenose100% (Occlusion)   -0.263251   0.945326  -0.278  0.78080   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.065 on 377 degrees of freedom
Multiple R-squared:  0.07117,   Adjusted R-squared:  0.03175 
F-statistic: 1.805 on 16 and 377 DF,  p-value: 0.02875

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' sICAM1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: sICAM1_rank 
Effect size...............: 0.143573 
Standard error............: 0.054927 
Odds ratio (effect size)..: 1.154 
Lower 95% CI..............: 1.037 
Upper 95% CI..............: 1.286 
T-value...................: 2.613889 
P-value...................: 0.009310471 
R^2.......................: 0.071168 
Adjusted r^2..............: 0.031748 
Sample size of AE DB......: 2421 
Sample size of model......: 394 
Missing data %............: 83.72573 

- processing VEGFA_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Med.Statin.LLD + GFR_MDRD + 
    BMI + stenose, data = currentDF)

Coefficients:
            (Intercept)                      Age        Med.Statin.LLDyes                 GFR_MDRD                      BMI            stenose50-70%            stenose70-90%  
               0.561958                -0.012663                -0.297486                -0.006419                -0.022865                 0.569596                 1.391138  
          stenose90-99%  stenose100% (Occlusion)  
               1.216539                 0.562266  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8596 -0.6291 -0.0838  0.4964  3.0093 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)  
(Intercept)                0.607522   1.345220   0.452   0.6519  
currentDF[, TRAIT]        -0.003457   0.057353  -0.060   0.9520  
Age                       -0.011991   0.007339  -1.634   0.1033  
Gendermale                 0.178602   0.128686   1.388   0.1662  
Hypertension.compositeyes -0.163322   0.178819  -0.913   0.3618  
DiabetesStatusDiabetes    -0.084955   0.143970  -0.590   0.5556  
SmokerStatusEx-smoker     -0.129825   0.128693  -1.009   0.3139  
SmokerStatusNever smoked   0.231649   0.203142   1.140   0.2550  
Med.Statin.LLDyes         -0.258439   0.132397  -1.952   0.0518 .
Med.all.antiplateletyes   -0.182370   0.195220  -0.934   0.3509  
GFR_MDRD                  -0.006750   0.003112  -2.169   0.0308 *
BMI                       -0.017707   0.016166  -1.095   0.2742  
MedHx_CVDyes               0.039904   0.122523   0.326   0.7449  
stenose50-70%              0.537706   1.093205   0.492   0.6232  
stenose70-90%              1.365104   1.046768   1.304   0.1932  
stenose90-99%              1.195350   1.044758   1.144   0.2534  
stenose100% (Occlusion)    0.378078   1.178266   0.321   0.7485  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.036 on 313 degrees of freedom
Multiple R-squared:  0.0856,    Adjusted R-squared:  0.03886 
F-statistic: 1.831 on 16 and 313 DF,  p-value: 0.02657

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VEGFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: VEGFA_rank 
Effect size...............: -0.003457 
Standard error............: 0.057353 
Odds ratio (effect size)..: 0.997 
Lower 95% CI..............: 0.891 
Upper 95% CI..............: 1.115 
T-value...................: -0.060281 
P-value...................: 0.9519706 
R^2.......................: 0.085598 
Adjusted r^2..............: 0.038856 
Sample size of AE DB......: 2421 
Sample size of model......: 330 
Missing data %............: 86.36927 

- processing TGFB_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age   Med.Statin.LLDyes            GFR_MDRD  
          1.075126            0.121559           -0.010900           -0.285644           -0.005516  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8652 -0.6771 -0.1268  0.4888  3.1195 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)  
(Intercept)                1.020467   1.108745   0.920   0.3580  
currentDF[, TRAIT]         0.131606   0.055751   2.361   0.0188 *
Age                       -0.012702   0.007078  -1.795   0.0735 .
Gendermale                 0.124424   0.123639   1.006   0.3149  
Hypertension.compositeyes -0.072924   0.166026  -0.439   0.6608  
DiabetesStatusDiabetes     0.081260   0.139664   0.582   0.5610  
SmokerStatusEx-smoker     -0.018945   0.124477  -0.152   0.8791  
SmokerStatusNever smoked   0.273180   0.191927   1.423   0.1555  
Med.Statin.LLDyes         -0.309384   0.128266  -2.412   0.0164 *
Med.all.antiplateletyes   -0.056329   0.197713  -0.285   0.7759  
GFR_MDRD                  -0.004745   0.003163  -1.500   0.1344  
BMI                       -0.016197   0.015407  -1.051   0.2938  
MedHx_CVDyes               0.125327   0.116593   1.075   0.2831  
stenose50-70%             -0.139359   0.821312  -0.170   0.8654  
stenose70-90%              0.559599   0.773324   0.724   0.4698  
stenose90-99%              0.483524   0.770196   0.628   0.5305  
stenose100% (Occlusion)   -0.348375   0.956225  -0.364   0.7158  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.075 on 367 degrees of freedom
Multiple R-squared:  0.07099,   Adjusted R-squared:  0.03049 
F-statistic: 1.753 on 16 and 367 DF,  p-value: 0.03591

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TGFB_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: TGFB_rank 
Effect size...............: 0.131606 
Standard error............: 0.055751 
Odds ratio (effect size)..: 1.141 
Lower 95% CI..............: 1.023 
Upper 95% CI..............: 1.272 
T-value...................: 2.3606 
P-value...................: 0.01876773 
R^2.......................: 0.07099 
Adjusted r^2..............: 0.030489 
Sample size of AE DB......: 2421 
Sample size of model......: 384 
Missing data %............: 84.13879 

- processing MMP2_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Med.Statin.LLD + GFR_MDRD, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age          Gendermale   Med.Statin.LLDyes            GFR_MDRD  
          0.860714            0.208459           -0.009766            0.180657           -0.265555           -0.005786  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2294 -0.7016 -0.1237  0.5027  3.0624 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                0.789092   1.102062   0.716  0.47443   
currentDF[, TRAIT]         0.197640   0.059853   3.302  0.00105 **
Age                       -0.011113   0.006940  -1.601  0.11012   
Gendermale                 0.208279   0.121321   1.717  0.08686 . 
Hypertension.compositeyes -0.010045   0.169165  -0.059  0.95268   
DiabetesStatusDiabetes     0.021362   0.137102   0.156  0.87627   
SmokerStatusEx-smoker     -0.070769   0.122503  -0.578  0.56382   
SmokerStatusNever smoked   0.212581   0.187413   1.134  0.25740   
Med.Statin.LLDyes         -0.276110   0.125543  -2.199  0.02847 * 
Med.all.antiplateletyes   -0.157313   0.198913  -0.791  0.42953   
GFR_MDRD                  -0.005032   0.003069  -1.640  0.10190   
BMI                       -0.016958   0.015325  -1.107  0.26921   
MedHx_CVDyes               0.133523   0.115436   1.157  0.24814   
stenose50-70%              0.105197   0.813586   0.129  0.89719   
stenose70-90%              0.684198   0.766829   0.892  0.37284   
stenose90-99%              0.629968   0.763582   0.825  0.40989   
stenose100% (Occlusion)   -0.235865   0.947909  -0.249  0.80363   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.067 on 372 degrees of freedom
Multiple R-squared:  0.08797,   Adjusted R-squared:  0.04874 
F-statistic: 2.242 on 16 and 372 DF,  p-value: 0.004115

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: MMP2_rank 
Effect size...............: 0.19764 
Standard error............: 0.059853 
Odds ratio (effect size)..: 1.219 
Lower 95% CI..............: 1.084 
Upper 95% CI..............: 1.37 
T-value...................: 3.302098 
P-value...................: 0.001052585 
R^2.......................: 0.087965 
Adjusted r^2..............: 0.048738 
Sample size of AE DB......: 2421 
Sample size of model......: 389 
Missing data %............: 83.93226 

- processing MMP8_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age   Med.Statin.LLDyes            GFR_MDRD  
           1.06102             0.14799            -0.01108            -0.26693            -0.00553  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1959 -0.7152 -0.1282  0.5110  3.2091 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)  
(Intercept)                1.482771   1.108685   1.337   0.1819  
currentDF[, TRAIT]         0.147885   0.058910   2.510   0.0125 *
Age                       -0.013465   0.006936  -1.941   0.0530 .
Gendermale                 0.108072   0.123868   0.872   0.3835  
Hypertension.compositeyes -0.128748   0.167537  -0.768   0.4427  
DiabetesStatusDiabetes     0.042865   0.138536   0.309   0.7572  
SmokerStatusEx-smoker     -0.026843   0.123288  -0.218   0.8278  
SmokerStatusNever smoked   0.297465   0.187586   1.586   0.1136  
Med.Statin.LLDyes         -0.281276   0.126283  -2.227   0.0265 *
Med.all.antiplateletyes   -0.073495   0.200015  -0.367   0.7135  
GFR_MDRD                  -0.005165   0.003091  -1.671   0.0956 .
BMI                       -0.024522   0.015541  -1.578   0.1155  
MedHx_CVDyes               0.149911   0.115941   1.293   0.1968  
stenose50-70%             -0.212904   0.823889  -0.258   0.7962  
stenose70-90%              0.395814   0.778460   0.508   0.6114  
stenose90-99%              0.375920   0.773158   0.486   0.6271  
stenose100% (Occlusion)   -0.544177   0.961255  -0.566   0.5717  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.073 on 372 degrees of freedom
Multiple R-squared:  0.07687,   Adjusted R-squared:  0.03717 
F-statistic: 1.936 on 16 and 372 DF,  p-value: 0.01647

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: MMP8_rank 
Effect size...............: 0.147885 
Standard error............: 0.05891 
Odds ratio (effect size)..: 1.159 
Lower 95% CI..............: 1.033 
Upper 95% CI..............: 1.301 
T-value...................: 2.510372 
P-value...................: 0.01248401 
R^2.......................: 0.076871 
Adjusted r^2..............: 0.037166 
Sample size of AE DB......: 2421 
Sample size of model......: 389 
Missing data %............: 83.93226 

- processing MMP9_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age   Med.Statin.LLDyes            GFR_MDRD  
           1.04087             0.13159            -0.01111            -0.27097            -0.00525  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1475 -0.6729 -0.1143  0.4851  3.0766 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)  
(Intercept)                1.248373   1.104524   1.130   0.2591  
currentDF[, TRAIT]         0.124657   0.055755   2.236   0.0260 *
Age                       -0.013384   0.006949  -1.926   0.0549 .
Gendermale                 0.133726   0.122695   1.090   0.2765  
Hypertension.compositeyes -0.085614   0.168061  -0.509   0.6108  
DiabetesStatusDiabetes     0.036571   0.138665   0.264   0.7921  
SmokerStatusEx-smoker     -0.033985   0.123377  -0.275   0.7831  
SmokerStatusNever smoked   0.273707   0.187789   1.458   0.1458  
Med.Statin.LLDyes         -0.288894   0.126441  -2.285   0.0229 *
Med.all.antiplateletyes   -0.112078   0.199903  -0.561   0.5754  
GFR_MDRD                  -0.004967   0.003113  -1.595   0.1115  
BMI                       -0.023436   0.015539  -1.508   0.1323  
MedHx_CVDyes               0.156463   0.116099   1.348   0.1786  
stenose50-70%             -0.010769   0.819811  -0.013   0.9895  
stenose70-90%              0.591452   0.773337   0.765   0.4449  
stenose90-99%              0.527537   0.770091   0.685   0.4937  
stenose100% (Occlusion)   -0.338213   0.956284  -0.354   0.7238  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.075 on 372 degrees of freedom
Multiple R-squared:  0.07368,   Adjusted R-squared:  0.03384 
F-statistic: 1.849 on 16 and 372 DF,  p-value: 0.02393

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: MMP9_rank 
Effect size...............: 0.124657 
Standard error............: 0.055755 
Odds ratio (effect size)..: 1.133 
Lower 95% CI..............: 1.015 
Upper 95% CI..............: 1.264 
T-value...................: 2.235786 
P-value...................: 0.02595834 
R^2.......................: 0.073679 
Adjusted r^2..............: 0.033838 
Sample size of AE DB......: 2421 
Sample size of model......: 389 
Missing data %............: 83.93226 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Session information


Version:      v1.0.9
Last update:  2020-06-26
Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description:  Script to analyse MCP1 from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_
* analysis on serum ASAP
* linear regression models (model 1 and model 2) of `MCP1_pg_ug_2015` with cytokines DONE
* check out the difference between the measuremens of `MCP1` and `MCP1_pg_ug_2015` MONDAY
* double check the plotting of the MACE DONE
* add the statistics for the correlation of `MCP1_pg_ug_2015` with the cytokines DONE

_S_
* prettify forest plot

_C_


_W_


**Changes log**
* v1.0.9 Added linear regression models for MCP1 vs. cytokines plaque levels. Double checked upload of MACE-plots. Added statistics from correlation (heatmap) to txt-file.
* v1.0.8 Fixed error in MCP1 serum analysis. It turns out the `MCP1` and `MCP1_pg_ug_2015` variables are _both_ measured in plaque, in two separate experiments, exp. no. 1 and exp. no. 2, respectively. 
* v1.0.7 Fixed the per Age-group MCP1 Box plots. Added correlations with other cytokines in plaques.
* v1.0.6 Only analyses and figures that end up in the final manuscript.
* v1.0.5 Update with 30- and 90-days survival.
* v1.0.4 Updated with Cox-regressions.
* v1.0.3 Included more models.
* v1.0.2 Bugs fixed.
* v1.0.1 Extended with linear and logistic regressions
* v1.0.0 Inital version

sessionInfo()

Saving environment

save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))
© 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | swvanderlaan.github.io.
---
title: "Athero-Express Biobank Study -- IL6 and MCP1 plaque levels."
author: '[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan; Marios Georgakis; Rainer Malik; Martin Dichgans'
date: '`r Sys.Date()`'
output:
  html_notebook:
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
mainfont: Helvetica
subtitle: An 'Athero-Express Biobank Study' project
editor_options:
  chunk_output_type: inline
---
```{r global_options, include = FALSE}
# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/',
                      eval = TRUE, warning = FALSE, message = FALSE)
```

# Preparation

Clean the environment.
```{r ClearEnvironment, include = FALSE}
rm(list = ls())
```

Set locations, and the working directory.
```{r LocalSystem, include = FALSE}
### Operating System Version
### Mac Pro
# ROOT_loc = "/Volumes/EliteProQx2Media"
# GENOMIC_loc = "/Users/svanderlaan/iCloud/Genomics"

### MacBook
ROOT_loc = "/Users/swvanderlaan"
GENOMIC_loc = paste0(ROOT_loc, "/iCloud/Genomics")

### Generic Locations
AEDB_loc = paste0(GENOMIC_loc, "/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")
RESULTS = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")
RAWDATA = paste0(ROOT_loc, "/PLINK/_AE_ORIGINALS/AESCRNA/prepped_data")
PROJECT_loc = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")

### SOME VARIABLES WE NEED DOWN THE LINE
cat("\nDefining phenotypes and datasets.\n")
PROJECTNAME="IL6MCP1"
# SUBPROJECTNAME=""

cat("\nCreate a new analysis directory, including subdirectories.\n")
# Analysis
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

# Plots
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

# QC plots
ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

# Output files
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

# COX analysis
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/COX")), 
       dir.create(file.path(ANALYSIS_loc, "/COX")), 
       FALSE)
COX_loc = paste0(ANALYSIS_loc, "/COX")

# Baseline characteristics
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")), 
       dir.create(file.path(ANALYSIS_loc, "/BASELINE")), 
       FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")

# Sample selection
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       dir.create(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       FALSE)
SELECTIONS_loc = paste0(ANALYSIS_loc, "/SELECTIONS")

cat("\nSetting working directory and listing its contents.\n")
setwd(paste0(PROJECT_loc))
getwd()
list.files()
```

A package-installation function.
```{r Function: installations, include = FALSE}
install.packages.auto <- function(x) { 
  x <- as.character(substitute(x)) 
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else { 
    # Update installed packages - this may mean a full upgrade of R, which in turn
    # may not be warrented. 
    # update.install.packages.auto(ask = FALSE) 
    eval(parse(text = sprintf("install.packages(\"%s\", dependencies = TRUE, repos = \"https://cloud.r-project.org/\")", x)))
  }
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else {
    if (!requireNamespace("BiocManager"))
      install.packages("BiocManager")
    # BiocManager::install() # this would entail updating installed packages, which in turned may not be warrented
    eval(parse(text = sprintf("BiocManager::install(\"%s\")", x)))
    eval(parse(text = sprintf("require(\"%s\")", x)))
  }
}
```

Load those packages.
```{r Setting: loading_packages, include = FALSE}
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("MASS")
# install.packages.auto("Seurat") # latest version

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

install.packages.auto("haven")
install.packages.auto("sjlabelled")
install.packages.auto("sjPlot")
install.packages.auto("labelled")
install.packages.auto("tableone")

install.packages.auto("ggpubr")

```

We will create a datestamp and define the Utrecht Science Park Colour Scheme.
```{r Setting: Colors, include = FALSE}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
### 
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

#ggplot2 default color palette
gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}

### ----------------------------------------------------------------------------
```


```{r Analysis Functions}
# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    tvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    R = summary(fit)$r.squared
    R.adj = summary(fit)$adj.r.squared
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
    
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("T-value...................:", round(tvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("R^2.......................:", round(R, 6), "\n")
    cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,9))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data...\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    zvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    dev <- fit$deviance
    nullDev <- fit$null.deviance
    modelN <- length(fit$fitted.values)
    R.l <- 1 - dev / nullDev
    R.cs <- 1 - exp(-(nullDev - dev) / modelN)
    R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("Z-value...................:", round(zvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
    cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
    cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}
```


# Background

Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b).
While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.


## Objectives

Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.


## Methods

*Blood*

OLINK-platform 

- IL6: Interleukin 6. Entrez Gene: 3569. OLINK, plasma <!-- Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL] -->
- MCP1: Monocyte chemotactic protein 1, MCP-1 (Chemokine (C-C motif) ligand 2, CCL2). Entrez Gene: 6347. OLINK, plasma <!-- Measured at the WKZ. Recalculated Luminex. [pg/mL] -->

> THESE DATA ARE NOT AVAILABLE YET

*Plaque*

Luminex-platform, measured by Luminex

- MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/ug]. Measured in two experiments, variables `MCP1` and `MCP1_pg_ug_2015`. We consider the latter the best possible measurement as this was corrected for plaque total protein concentration.
- IL6: Interleuking 6 (IL6; Entrez Gene: 3569) concentration in plaque [pg/ug].
- IL6R: Interleuking 6 receptor (IL6R; Entrez Gene: 3570) concentration in plaque [pg/ug].

FACS platform

- IL6: Interleukin 6. Entrez Gene: 3569. Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL]


# Loading data

## Clinical data

Loading Athero-Express clinical data.
```{r LoadAEDB}
require(haven)

# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))

head(AEDB)



```

#### Examine AEDB

We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class (type) it has, and what the variable description is. 

There is an excellent post on this: https://www.r-bloggers.com/working-with-spss-labels-in-r/. 
```{r AEDB: describe}
AEDB %>% sjPlot::view_df(show.type = TRUE,
                         show.frq = TRUE,
                         show.prc = TRUE,
                         show.na = TRUE, 
                         max.len = TRUE, 
                         wrap.labels = 20,
                         verbose = FALSE, 
                         use.viewer = FALSE,
                         file = paste0(OUT_loc, "/", Today, ".AEDB.dictionary.html")) 
```


## Fixing and creating variables

We need to be very strict in defining _symptoms._ Therefore we will fix a new variable that groups _symptoms_ at inclusion.

Coding of _symptoms_ is as follows:

- missing	-999	
- Asymptomatic	0	
- TIA	1	
- minor stroke	2	
- Major stroke	3	
- Amaurosis fugax	4	
- Four vessel disease	5	
- Vertebrobasilary TIA	7	
- Retinal infarction	8	
- Symptomatic, but aspecific symtoms	9
- Contralateral symptomatic occlusion	10	
- retinal infarction	11	
- armclaudication due to occlusion subclavian artery, CEA needed for bypass	12	
- retinal infarction + TIAs	13	
- Ocular ischemic syndrome	14	
- ischemisch glaucoom	15	
- subclavian steal syndrome	16	
- TGA	17

We will group as follows in `Symptoms.5G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13
3. Stroke > 2, 3
4. Ocular > 4, 14, 15
5. Retinal infarction > 8, 11
6. Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3 
3. Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt2G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3 Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17


```{r FixSymptoms, message=FALSE, warning=FALSE}
# Fix symptoms

attach(AEDB)

AEDB$sympt[is.na(AEDB$sympt)] <- -999

# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"

# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"

# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"

detach(AEDB)

# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
# 
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
# 
# rm(AEDB.temp)

```

We will also fix the _plaquephenotypes_ variable.  

Coding of symptoms is as follows:

- missing	-999	
- not relevant -888
- fibrous	1	
- fibroatheromatous	2	
- atheromatous	3	


```{r FixPlaquePhenotypes, message=FALSE, warning=FALSE}

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

table(AEDB$OverallPlaquePhenotype)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```

We will also fix the _diabetes_ status variable. We define diabetes as history of a diagnosis and/or use of glucose-lowering medications.

```{r FixDiabetes, message=FALSE, warning=FALSE}
# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

table(AEDB$DM.composite)

table(AEDB$DiabetesStatus)


# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```


We will also fix the _smoking_ status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire. 

- `diet801`: are you a smoker?
- `diet802`: did you smoke in the past?

We already have some variables indicating smoking status:

- `SmokingReported`: patient has reported to smoke.
- `SmokingYearOR`: smoking in the year of surgery?
- `SmokerCurrent`: currently smoking?



```{r FixSmoking, message=FALSE, warning=FALSE}
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)

# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
# 
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))

cat("\nFixing smoking status.\n")
attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)

cat("\n* Current smoking status.\n")
table(AEDB$SmokerCurrent,
      useNA = "ifany", 
      dnn = c("Current smoker"))

cat("\n* Updated smoking status.\n")
table(AEDB$SmokerStatus,
      useNA = "ifany", 
      dnn = c("Updated smoking status"))

cat("\n* Comparing to 'SmokerCurrent'.\n")
table(AEDB$SmokerStatus, AEDB$SmokerCurrent, 
      useNA = "ifany", 
      dnn = c("Updated smoking status", "Current smoker"))

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix the _alcohol_ status variable.


```{r FixAlcohol, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)

table(AEDB$AlcoholUse)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on `CAD_history`, `Stroke_history`, and `Peripheral.interv`

```{r FixCAD_History, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)

table(AEDB$CAD_history)
table(AEDB$Stroke_history)
table(AEDB$Peripheral.interv)
table(AEDB$MedHx_CVD)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```




# Athero-Express Biobank Study

## Baseline characteristics

We are interested in the following variables at baseline.

- Age (years)
- Female sex (N, %)
- Hypertension (N, %)
- SBP (mmHg)
- DBP (mmHg)
- Diabetes mellitus (N, %)
- Total cholesterol levels (mg/dL)
- LDL cholesterol levels (mg/dL)
- HDL cholesterol levels (mg/dL)
- Triglyceride levels (mg/dL)
- Use of statins (N, %)
- Use of antiplatelet drugs (N, %)
- BMI (kg/m²)
- Smoking status (N, %)
  - Never smokers
  - Ex-smokers
  - Current smokers
- History of CAD (N, %)
- History of PAD (N, %)
- Clinical manifestations
  - Asymptomatic
  - Amaurosis fugax
  - TIA
  - Stroke
- eGFR (mL/min/1.73 m²)
- MCP-1 plaque levels (pg/mL) (LUMINEX based, two experiments `MCP1`, and `MCP1_pg_ug_2015`)
- MCP-1 serum levels (pg/mL) (OLINK based)


```{r Baseline AEDB: creation, include = FALSE}
cat("====================================================================================================\n")
cat("SELECTION THE SHIZZLE\n")

### Artery levels
# AEdata$Artery_summary: 
#           value                                                                                   label
# NOT USE - 0 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
# USE - 1                                                                  carotid (left & right)
# USE - 2                                               femoral/iliac (left, right or both sides)
# NOT USE - 3                                               other carotid arteries (common, external)
# NOT USE - 4                                   carotid bypass and injury (left, right or both sides)
# NOT USE - 5                                                         aneurysmata (carotid & femoral)
# NOT USE - 6                                                                                   aorta
# NOT USE - 7                                            other arteries (renal, popliteal, vertebral)
# NOT USE - 8                        femoral bypass, angioseal and injury (left, right or both sides)

### AEdata$informedconsent
#           value                                                                                           label
# NOT USE - -999                                                                                         missing
# NOT USE - 0                                                                                        no, died
# USE - 1                                                                                             yes
# USE - 2                                                             yes, health treatment when possible
# USE - 3                                                                        yes, no health treatment
# USE - 4                                                yes, no health treatment, no commercial business
# NOT USE - 5                                                          yes, no tissue, no commerical business
# NOT USE - 6                      yes, no tissue, no questionnaires, no medical info, no commercial business
# USE - 7                             yes, no questionnaires, no health treatment, no commercial business
# USE - 8                                          yes, no questionnaires, health treatment when possible
# NOT USE - 9                  yes, no tissue, no questionnaires, no health treatment, no commerical business
# USE - 10                               yes, no health treatment, no medical info, no commercial business
# NOT USE - 11 yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business
# USE - 12                                                     yes, no questionnaires, no health treatment
# NOT USE - 13                                                             yes, no tissue, no health treatment
# NOT USE - 14                                                               yes, no tissue, no questionnaires
# NOT USE - 15                                                  yes, no tissue, health treatment when possible
# NOT USE - 16                                                                                  yes, no tissue
# USE - 17                                                                     yes, no commerical business
# USE - 18                                     yes, health treatment when possible, no commercial business
# USE - 19                                                    yes, no medical info, no commercial business
# USE - 20                                                                          yes, no questionnaires
# NOT USE - 21                         yes, no tissue, no questionnaires, no health treatment, no medical info
# NOT USE - 22                  yes, no tissue, no questionnaires, no health treatment, no commercial business
# USE - 23                                                                            yes, no medical info
# USE - 24                                                  yes, no questionnaires, no commercial business
# USE - 25                                    yes, no questionnaires, no health treatment, no medical info
# USE - 26                  yes, no questionnaires, health treatment when possible, no commercial business
# USE - 27                                                      yes,  no health treatment, no medical info
# NOT USE - 28                                                                             no, doesn't want to
# NOT USE - 29                                                                              no, unable to sign
# NOT USE - 30                                                                                 no, no reaction
# NOT USE - 31                                                                                        no, lost
# NOT USE - 32                                                                                     no, too old
# NOT USE - 34                                            yes, no medical info, health treatment when possible
# NOT USE - 35                                             no (never asked for IC because there was no tissue)
# USE - 36                    yes, no medical info, no commercial business, health treatment when possible
# NOT USE - 37                                                                                    no, endpoint
# USE - 38                                                         wil niets invullen, wel alles gebruiken
# USE - 39                                           second informed concents: yes, no commercial business
# NOT USE - 40                                                                              nooit geincludeerd

cat("- sanity checking PRIOR to selection")
library(data.table)
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
ae.hospital <- ifelse(AEDB$Hospital == 1, "Antonius", "UMCU")
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"))
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
table(ae.gender, AEDB$Artery_summary, dnn = c("Sex", "Artery"))
# table(ae.gender, AEDB$informedconsent, dnn = c("Sex", "IC"))

rm(ae.gender, ae.hospital)

# I change numeric and factors manually because, well, I wouldn't know how to fix it otherwise
# to have this 'tibble' work with 'tableone'... :-)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$diastoli <- as.numeric(AEDB$diastoli)
AEDB$systolic <- as.numeric(AEDB$systolic)

AEDB$TC_finalCU <- as.numeric(AEDB$TC_finalCU)
AEDB$LDL_finalCU <- as.numeric(AEDB$LDL_finalCU)
AEDB$HDL_finalCU <- as.numeric(AEDB$HDL_finalCU)
AEDB$TG_finalCU <- as.numeric(AEDB$TG_finalCU)

AEDB$TC_final <- as.numeric(AEDB$TC_final)
AEDB$LDL_final <- as.numeric(AEDB$LDL_final)
AEDB$HDL_final <- as.numeric(AEDB$HDL_final)
AEDB$TG_final <- as.numeric(AEDB$TG_final)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$GFR_MDRD <- as.numeric(AEDB$GFR_MDRD)
AEDB$BMI <- as.numeric(AEDB$BMI)
AEDB$eCigarettes <- as.numeric(AEDB$eCigarettes)
AEDB$ePackYearsSmoking <- as.numeric(AEDB$ePackYearsSmoking)
AEDB$EP_composite_time <- as.numeric(AEDB$EP_composite_time)

AEDB$macmean0 <- as.numeric(AEDB$macmean0)
AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)

# IL6, IL6R, MCP1 measurements
AEDB$IL6 <- as.numeric(AEDB$IL6) # FACS plaque
AEDB$IL6_pg_ug_2015 <- as.numeric(AEDB$IL6_pg_ug_2015) # LUMINEX plaque
AEDB$IL6R_pg_ug_2015 <- as.numeric(AEDB$IL6R_pg_ug_2015) # LUMINEX plaque
AEDB$MCP1 <- as.numeric(AEDB$MCP1) # LUMINEX plaque
AEDB$MCP1_pg_ug_2015 <- as.numeric(AEDB$MCP1_pg_ug_2015) # LUMINEX plaque
AEDB$hsCRP_plasma <- as.numeric(AEDB$hsCRP_plasma) # LUMINEX

require(labelled)
AEDB$ORyear <- to_factor(AEDB$ORyear)
AEDB$Gender <- to_factor(AEDB$Gender)
AEDB$Hospital <- to_factor(AEDB$Hospital)
AEDB$KDOQI <- to_factor(AEDB$KDOQI)
AEDB$BMI_WHO <- to_factor(AEDB$BMI_WHO)
AEDB$DiabetesStatus <- to_factor(AEDB$DiabetesStatus)
AEDB$SmokerStatus <- to_factor(AEDB$SmokerStatus)
AEDB$AlcoholUse <- to_factor(AEDB$AlcoholUse)

AEDB$Hypertension.selfreport <- to_factor(AEDB$Hypertension1)
AEDB$Hypertension.selfreportdrug <- to_factor(AEDB$Hypertension2)
AEDB$Hypertension.composite <- to_factor(AEDB$Hypertension.composite)
AEDB$Hypertension.drugs <- to_factor(AEDB$Hypertension.drugs)

AEDB$Med.anticoagulants <- to_factor(AEDB$Med.anticoagulants)
AEDB$Med.all.antiplatelet <- to_factor(AEDB$Med.all.antiplatelet)
AEDB$Med.Statin.LLD <- to_factor(AEDB$Med.Statin.LLD)

AEDB$Stroke_Dx <- to_factor(AEDB$Stroke_Dx)
AEDB$CAD_history <- to_factor(AEDB$CAD_history)
AEDB$PAOD <- to_factor(AEDB$PAOD)
AEDB$Peripheral.interv <- to_factor(AEDB$Peripheral.interv)
AEDB$MedHx_CVD <- to_factor(AEDB$MedHx_CVD)


AEDB$sympt <- to_factor(AEDB$sympt)
AEDB$Symptoms.3g <- to_factor(AEDB$Symptoms.3g)
AEDB$Symptoms.4g <- to_factor(AEDB$Symptoms.4g)
AEDB$Symptoms.5G <- to_factor(AEDB$Symptoms.5G)
AEDB$AsymptSympt <- to_factor(AEDB$AsymptSympt)
AEDB$AsymptSympt2G <- to_factor(AEDB$AsymptSympt2G)


AEDB$restenos <- to_factor(AEDB$restenos)
AEDB$stenose <- to_factor(AEDB$stenose)
AEDB$EP_composite <- to_factor(AEDB$EP_composite)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
AEDB$OverallPlaquePhenotype <- to_factor(AEDB$OverallPlaquePhenotype)

AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)

AEDB$informedconsent <- to_factor(AEDB$informedconsent)

AEDB.CEA <- subset(AEDB,
                    (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & # we only want carotids
                       informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd" & 
                     !is.na(AsymptSympt2G))
# AEDB.CEA[1:10, 1:10]
dim(AEDB.CEA)
```

```{r}
cat("===========================================================================================\n")
cat("CREATE BASELINE TABLE\n")

# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
                   "Age", "Gender", 
                   "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "restenos", "stenose",
                   "MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6", "IL6_pg_ug_2015", "IL6R_pg_ug_2015",
                   "MCP1", "MCP1_pg_ug_2015")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con
```

### All patients
Showing the baseline table of the whole Athero-Express Biobank.

```{r Baseline AEDB: Visualize AEDB}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```

### CEA patients

Showing the baseline table of the CEA patients in the Athero-Express Biobank.

```{r Baseline AEDB: Visualize AEDB CEA}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```

### CEA patients with `MCP1_pg_ug_2015`

Showing the baseline table of the CEA patients in the Athero-Express Biobank with `MCP1_pg_ug_2015`.

```{r Baseline AEDB: Visualize subsetCEA}
AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015))

AEDB.CEA.subset.AsymptSympt.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]
```

### CEA patients with `MCP1_pg_ug_2015` and `MCP1`

Showing the baseline table of the CEA patients in the Athero-Express Biobank with `MCP1_pg_ug_2015` _and_ `MCP1`.

```{r Baseline AEDB: Visualize subsetCEA with MCP1}

AEDB.CEA.subset.combo <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) | !is.na(MCP1))

AEDB.CEA.subset.combo.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.combo, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]
```

### CEA patients with plasma MCP1 levels

Showing the baseline table of the CEA patients in the Athero-Express Biobank with plasma MCP1 levels.

> NOT AVAILABLE YET

```{r Baseline AEDB: Visualize subsetCEA with serum MCP1}
AEDB.CEA.subset.serum <- subset(AEDB.CEA, !is.na(MCP1_plasma))

AEDB.CEA.subset.serum.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.serum, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]
```


### CEA patients with plasma _and_ plaque MCP1 levels

Showing the baseline table of the CEA patients in the Athero-Express Biobank with both plasma and plaque MCP1 levels.

> NOT AVAILABLE YET

```{r Baseline AEDB: Visualize subsetCEA with serum MCP1 and plaque MCP1}
AEDB.CEA.subset.both <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) & !is.na(MCP1))

AEDB.CEA.subset.both.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.both, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]
```

Writing the baseline table to Excel format. 
```{r Baseline AEDB: write}
# Write basetable
require(openxlsx)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
           AEDB.CEA.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.AsymptSympt.xlsx"),
           AEDB.CEA.subset.AsymptSympt.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline_Sympt")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
           AEDB.CEA.subset.combo.tableOne,
           row.names = TRUE,
           col.names = TRUE,
           sheetName = "subsetAEDB_Baseline")

# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEAserum.AsymptSympt.xlsx"),
#            AEDB.CEA.subset.serum.tableOne, 
#            row.names = TRUE, 
#            col.names = TRUE, 
#            sheetName = "subsetAEDB_Baseline_serum_Sympt")

```


## Data exploration

Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, natural log transformed + the smallest measurement, and inverse-normal transformation. 

### MCP1 plaque levels 

We will explore the plaque levels. As noted above, we will use `MCP1_pg_ug_2015`.

```{r DataExploration: MCP1 plaque}

summary(AEDB.CEA$MCP1_pg_ug_2015)

do.call(rbind , by(AEDB.CEA$MCP1_pg_ug_2015, AEDB.CEA$AsymptSympt2G, summary))

```

```{r DataExploration: MCP1 plaque visual}
library(patchwork)
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())

min_MCP1_pg_ug_2015 <- min(AEDB.CEA$MCP1_pg_ug_2015, na.rm = TRUE)
min_MCP1_pg_ug_2015

AEDB.CEA$MCP1_pg_ug_2015_LN <- log(AEDB.CEA$MCP1_pg_ug_2015 + min_MCP1_pg_ug_2015)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    # title = "MCP1 plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ug_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/ug",
                    ggtheme = theme_minimal())

p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)
```


### MCP1 serum levels 

> NOT AVAILABLE YET

```{r DataExploration: MCP1 serum}

summary(AEDB.CEA$MCP1)

do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
do.call(rbind , by(AEDB.CEA.subset.serum$MCP1_pg_ug_2015, AEDB.CEA.subset.serum$AsymptSympt2G, summary))
do.call(rbind , by(AEDB.CEA.subset.serum$MCP1, AEDB.CEA.subset.serum$AsymptSympt2G, summary))

```

```{r DataExploration: MCP1 serum visual}
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())

min_MCP1 <- min(AEDB.CEA$MCP1, na.rm = TRUE)
min_MCP1

AEDB.CEA$MCP1_LN <- log(AEDB.CEA$MCP1 + min_MCP1)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 serum levels",
                    xlab = "inverse-normal transformation pg/ug",
                    ggtheme = theme_minimal())

p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)

```

## Preliminary conclusion data exploration

In line with the previous work by [Marios Georgakis](https://www.ahajournals.org/doi/full/10.1161/CIRCRESAHA.119.315380){target="_blank"} we will apply _natural log transformation_ on all proteins and focus the analysis on MCP1 in serum and plaque.

# Analyses

The analyses are focused on three elements: 

1) plaque vulnerability phenotypes
2) clinical status at inclusion (symptoms)
3) secondary clinical outcome during three (3) years of follow-up

## Covariates & other variables

1.  Age (continuous in 1-year increment). [`Age`]
2.  Sex (male vs. female). [`Gender`]
3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [`Hypertension.composite`]
4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [`DiabetesStatus`]
5.  Smoking (current, ex-, never). [`SmokerStatus`]
6.  LDL-C levels (continuous). [`LDL_final`]
7.  Use of lipid-lowering drugs. [`Med.Statin.LLD`]
8.  Use of antiplatelet drugs. [`Med.all.antiplatelet`]
9.  eGFR (continuous). [`GFR_MDRD`]
10.	BMI (continuous). [`BMI`]
11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [`MedHx_CVD`] combination of [`CAD_history`, `Stroke_history`, `Peripheral.interv`]
12.	Level of stenosis (50-70% vs. 70-99%). [`stenose`]

## Models

We will analyze the data through four different models

- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)

## A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque and serum MCP1, IL6, and IL6R levels we will focus on the following plaque vulnerability phenotypes:

- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)

*Continous traits*
```{r CrossSec: plaques - transformations and visualisations continuous}

# macrophages
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)

ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# smooth muscle cells
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)

ggpubr::gghistogram(AEDB.CEA, "SMC_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# vessel density
cat("Summary of data.\n")
summary(AEDB.CEA$vessel_density_averaged)

min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)

ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                    xlab = "natural log-transformed number", 
                    ggtheme = theme_minimal())

AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
```

*Binary traits*
```{r CrossSec: plaques - transformations and visualisations binary}

# calcification
cat("Summary of data.\n")
summary(AEDB.CEA$Calc.bin)
contrasts(AEDB.CEA$Calc.bin)

AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CalcificationPlaque)) %>%
  group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())
rm(df)

# collagen
cat("Summary of data.\n")
summary(AEDB.CEA$Collagen.bin)
contrasts(AEDB.CEA$Collagen.bin)

AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CollagenPlaque)) %>%
  group_by(Gender, CollagenPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())
rm(df)

# fat 10%
cat("Summary of data.\n")
summary(AEDB.CEA$Fat.bin_10)
contrasts(AEDB.CEA$Fat.bin_10)

AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)

df <- AEDB.CEA %>%
  filter(!is.na(Fat10Perc)) %>%
  group_by(Gender, Fat10Perc) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())
rm(df)

# IPH
cat("Summary of data.\n")
summary(AEDB.CEA$IPH.bin)
contrasts(AEDB.CEA$IPH.bin)

AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)

df <- AEDB.CEA %>%
  filter(!is.na(IPH)) %>%
  group_by(Gender, IPH) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())
rm(df)

# Symptoms
cat("Summary of data.\n")
summary(AEDB.CEA$AsymptSympt)
contrasts(AEDB.CEA$AsymptSympt)

AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)

df <- AEDB.CEA %>%
  filter(!is.na(AsymptSympt)) %>%
  group_by(Gender, AsymptSympt) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())
rm(df)

```

In this section we make some variables to assist with analysis.
```{r CrossSec: plaques - setup regression }
AEDB.CEA.samplesize = nrow(AEDB.CEA)
# TRAITS.PROTEIN = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN")
# TRAITS.PROTEIN.RANK = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank")
# TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank", "MCP1_rank")
TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank")

# TRAITS.CON = c("Macrophages_LN", "SMC_LN", "VesselDensity_LN") 
TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "VesselDensity_rank")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH")

# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.	BMI (continuous). [BMI]
# 11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12.	Level of stenosis (50-70% vs. 70-99%). [stenose]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,

COVARIATES_M1 = c("Age", "Gender")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", 
               "SmokerStatus", 
               # "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               # "CAD_history", "Stroke_history", "Peripheral.interv", 
               "MedHx_CVD",
               "stenose")

# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

# COVARIATES_M5 = c(COVARIATES_M2, "IL6_pg_ug_2015_LN")
# COVARIATES_M5rank = c(COVARIATES_M2, "IL6_pg_ug_2015_rank")

```

### Model 1

In this model we correct for _Age_ and _Gender_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

#### Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

#### Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque and serum MCP1, IL6, and IL6R levels and the 'clinical status' of the plaque in terms of presence of patients' symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic

### Model 1

In this model we correct for _Age_, and _Gender_.


```{r CrossSec: symptoms - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.


```{r CrossSec: symptoms - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                MedHx_CVD + + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque and serum MCP1, IL6, and IL6R levels and secondary cardiovascular events over a three-year follow-up period. 

The _primary outcome_ is defined as "a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death", i.e. major adverse cardiovascular events (MACE). Variable: `epmajor.3years`, these include:
- myocardial infarction (MI)
- cerebral infarction (CVA/stroke)
- cardiovascular death (exact cause to be investigated)
- cerebral bleeding (CVA/stroke)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..

The _secondary outcomes_ will be 

- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: `epstroke.3years`, these include:
  - cerebral infarction (CVA/stroke)
  - cerebral bleeding (CVA/stroke)
  - fatal cerebral infarction
  - fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: `epcoronary.3years`, these include:
  - myocardial infarction (MI)
  - coronary angioplasty (PCI/PTCA)
  - cardiovascular death (exact cause to be investigated)
  - coronary bypass (CABG)
  - fatal myocardial infarction (MI)
  - sudden death.
- cardiovascular death - variable: `epcvdeath.3years`, these include:
  - cardiovascular death (exact cause to be investigated)
  - fatal myocardial infarction (MI)
  - fatal cerebral infarction
  - fatal cerebral bleeding
  - sudden death
  - fatal heart failure
  - fatal aneurysm rupture
  - other cardiovascular death..

### 30- and 90-days FU events

We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up 'time-to-event' variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up. 

```{r Calculate new FU cut-offs: maximum FU}
cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90

# Fix maximum FU of 30 and 90 days
AEDB <- AEDB %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)


```

Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:

- MACE (`epmajor.3years`)
- Stroke (`epstroke.3years`)
- Coronary events (`epcoronary.3years`)
- Cardiovascular death (`epcvdeath.3years`)


```{r Calculate new FU cut-offs: times}
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times
AEDB <- AEDB %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

```


```{r Cox-regressions: new times}

attach(AEDB)
AEDB[,"epmajor.30days"] <- AEDB$epmajor.3years
AEDB$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB[,"epstroke.30days"] <- AEDB$epstroke.3years
AEDB$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB[,"epcoronary.30days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB[,"epcvdeath.30days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB[,"epmajor.90days"] <- AEDB$epmajor.3years
AEDB$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB[,"epstroke.90days"] <- AEDB$epstroke.3years
AEDB$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB[,"epcoronary.90days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB[,"epcvdeath.90days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

attach(AEDB.CEA)
AEDB.CEA[,"epmajor.30days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epstroke.30days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcoronary.30days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcvdeath.30days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epmajor.90days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epstroke.90days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcoronary.90days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcvdeath.90days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB.CEA)

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)



```

### Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.
```{r Cox-regressions: General}
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")

cat("* Creating function to summarize Cox regression and prepare container for results.")
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
  cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(protein, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", protein, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints){
  require(labelled)
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}

times30 = c("ep_major_t_30days", 
          "ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")

endpoints30 = c("epmajor.30days", 
              "epstroke.30days", "epcoronary.30days", "epcvdeath.30days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints30){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times30){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times30){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}

times90 = c("ep_major_t_90days", 
          "ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")

endpoints90 = c("epmajor.90days", 
              "epstroke.90days", "epcoronary.90days", "epcvdeath.90days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints90){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times90){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times90){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}


```


### Cox regressions

Let's perform the actual Cox-regressions. We will apply a couple of models: 

- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis

#### 3 years follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```


#### 30-days follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```


#### 90-days follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```



# Correlations

## All biomarkers
We correlated serum and plaque levels of the biomarkers.

```{r CrossSampleType Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_pg_ug_2015_rank",
                                               TRAITS.BIN, TRAITS.CON.RANK)
                                    )


AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)

# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}


chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)


# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:

# ggpairs(AEDB.CEA, 
#         columns = c("MCP1_rank", "MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK), 
#         columnLabels = c("MCP1 (serum)", "MCP1", 
#                          "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
#         method = c("spearman"),
#         # ggplot2::aes(colour = Gender),
#         progress = FALSE) 

ggpairs(AEDB.CEA,
        columns = c("MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK),
        columnLabels = c("MCP1",
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE)

```

## Circulating MCP1

Finally, we explored in a sub-sample, where circulating MCP-1 levels are available, the following:

1. A correlation between MCP-1 levels in the plaque and circulating MCP-1 levels
2. Associations of circulating MCP-1 levels with plaque vulnerability characteristics
3. Associations of circulating MCP-1 levels with the status of the plaque in terms of presence of symptoms (symptomatic vs. asymptomatic)
4. Associations of circulating MCP-1 levels with the primary composite endpoint of secondary cardiovascular events.

> NOT AVAILABLE YET

```{r MCP1 Serum Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_rank", 
                                     TRAITS.BIN, TRAITS.CON.RANK, 
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.serum.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers_serum.rank <- round(cor(AEDB.CEA.matrix.serum.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_serum_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.serum.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_serum.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_serum_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}


corr_biomarkers_serum.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_serum.rank, corr_biomarkers_serum_p.rank))
DT::datatable(corr_biomarkers_serum.rank.df)

# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}

chart.Correlation.new(AEDB.CEA.matrix.serum.RANK, method = "spearman", histogram = TRUE, pch = 3)


# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:
ggpairs(AEDB.CEA,
        columns = c("MCP1_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"), 
        columnLabels = c("MCP1 (serum)", 
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE) 
```

# Additional figures

## Age and sex
We want to create per-age-group figures. 

- Box and Whisker plot for MCP-1 plaque levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Box and Whisker plot for MCP-1 serum levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Scatter plot of the correlation and regression line between MCP-1 levels in plaque (y axis) and serum (x axis).


```{r AgeGroups}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
                                                     Age >= 55  & Age <= 64 ~ "55-64",
                                                     Age >= 65  & Age <= 74 ~ "65-74",
                                                     Age >= 75  & Age <= 84 ~ "75-84",
                                                     Age >= 85 ~ "85+"))) 

AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
                                                        Age >= 55  & Age <= 64 & Gender == "male"~ "55-64 males",
                                                        Age >= 65  & Age <= 74 & Gender == "male"~ "65-74 males",
                                                        Age >= 75  & Age <= 84 & Gender == "male"~ "75-84 males",
                                                        Age >= 85 & Gender == "male"~ "85+ males",
                                                        Age < 55 & Gender == "female" ~ "<55 females" ,
                                                        Age >= 55  & Age <= 64 & Gender == "female"~ "55-64 females ",
                                                        Age >= 65  & Age <= 74 & Gender == "female"~ "65-74 females",
                                                        Age >= 75  & Age <= 84 & Gender == "female"~ "75-84 females",
                                                        Age >= 85 & Gender == "female"~ "85+ females")))

table(AEDB.CEA$AgeGroup, AEDB.CEA$Gender)
table(AEDB.CEA$AgeGroupSex)

```

Now we can draw some graphs of serum/plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per AgeGroup per Sex, ranked}

# ?ggpubr::ggboxplot()

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
```

#### MCP1 serum levels

> NOT AVAILABLE YET

```{r MCP1 per AgeGroup per Sex, ranked serum}
# compare_means(MCP1_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("Gender"),
                  y = "MCP1_rank",
                  xlab = "Gender",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("AgeGroup"),
                  y = "MCP1_rank",
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per AgeGroup per Sex}

# ?ggpubr::ggboxplot()
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
```

### MCP1 serum levels

> NOT AVAILABLE YET

```{r MCP1 per AgeGroup per Sex, serum}
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1", 
                  xlab = "Gender",
                  ylab = "MCP1 serum [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 serum [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
```

## Circulating vs. plaque MCP1 levels

We will also make a nice correlation plot between serum and plaque MCP1 levels.

> NOT AVAILABLE YET

```{r MCP1 correlations, serum vs. plaque}

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_pg_ug_2015",
                  y = "MCP1", 
                  xlab = "MCP1 plaque [pg/ug]",
                  ylab = "MCP1 serum [pg/mL]",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(8,750))

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_pg_ug_2015_rank",
                  y = "MCP1_rank", 
                  xlab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(2,3))

```

## Plaque vs. plaque MCP1 levels
We will also make a nice correlation plot between the two experiments of plaque MCP1 levels. 

```{r MCP1 correlations, plaque vs. plaque}
AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
summary(AEDB.CEA$MCP1)
summary(AEDB.CEA$MCP1_pg_ug_2015)

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1", 
                  y = "MCP1_pg_ug_2015",
                  xlab = "MCP1 plaque [pg/mL] (exp. no. 1)",
                  ylab = "MCP1 plaque [pg/ug] (exp. no. 2)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_rank", 
                  y = "MCP1_pg_ug_2015_rank",
                  xlab = "MCP1 plaque [pg/mL]\n(INRT,  exp. no. 1)",
                  ylab = "MCP1 plaque [pg/ug]\n(INRT,  exp. no. 2)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))

```


## Symptoms
We want to create per-symptom figures. 


```{r SymptomGroups}
library(dplyr)

table(AEDB.CEA$AgeGroup, AEDB.CEA$AsymptSympt2G)
table(AEDB.CEA$Gender, AEDB.CEA$AsymptSympt2G)
table(AEDB.CEA$AsymptSympt2G)

```

Now we can draw some graphs of serum/plaque MCP1 levels per symptom group.
```{r MCP1 per SymptomGroups}

# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_pg_ug_2015_rank",
                  title = "MCP1 plaque [pg/ug] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 plaque [pg/ug]\n inverse-rank transformation",
                  color = "AsymptSympt2G", 
                  palette = c(uithof_color[16], uithof_color[23]),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
```

```{r MCP1 per SymptomGroups, serum}
p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_rank",
                  title = "MCP1 serum [pg/mL] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 serum [pg/mL]\n inverse-rank transformation",
                  color = "AsymptSympt2G", 
                  palette = c(uithof_color[16], uithof_color[23]),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
rm(p1)
```

## Forest plots

We would also like to visualize the multivariable analyses results.
```{r load model data}
library(ggplot2)
library(openxlsx)
model1_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"))
model2_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"))
model1_mcp1$model <- "univariate"
model2_mcp1$model <- "multivariate"

models_mcp1 <- rbind(model1_mcp1, model2_mcp1)
models_mcp1

```

```{r forestplot plaque}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]))

fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
  geom_pointrange() + 
  geom_hline(yintercept=1, lty=2) +  # add a dotted line at x=1 after flip
  coord_flip() +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  theme(text = element_text(size=14)) +
  ggtitle("Plaque MCP-1 levels (1 SD increment)") +
  theme_minimal()  # use a white background
print(fp)
rm(fp)
```


```{r forestplot serum}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_rank"]))

fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
  geom_pointrange() + 
  geom_hline(yintercept=1, lty=2) +  # add a dotted line at x=1 after flip
  coord_flip() +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  theme(text = element_text(size=14)) +
  ggtitle("Serum MCP-1 levels (1 SD increment)") +
  theme_minimal()  # use a white background
print(fp)
rm(fp)
```

## MCP1 vs. cytokines plaque levels correlations

We will plot the correlations of other cytokine plaque levels to the MCP1 plaque levels. These include:

- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB

In addition we will look at three metalloproteinases which were measured using an activity assay. 

- MMP2
- MMP8
- MMP9

The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the MCP1 plaque levels.


We will set the measurements that yielded '0' to NA, as it is unlikely that any protein ever has exactly 0 copies. The '0' yielded during the experiment are due to the limits of the detection.

### Prepare data
```{r MCP1 vs Cytokines INRT}
cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21", 
               "INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1", 
               "TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")

# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"


proteins_of_interest <- c(cytokines, metalloproteinases)

proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))


# make variables numerics()
AEDB.CEA <- AEDB.CEA %>%
  mutate_each(funs(as.numeric), proteins_of_interest)
  
for(PROTEIN in 1:length(proteins_of_interest)){

  # UCORBIOGSAqc$Z <- NULL
  var.temp.rank = proteins_of_interest_rank[PROTEIN]
  var.temp = proteins_of_interest[PROTEIN]
  
  cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
  cat(paste0("* changing ", var.temp, " to numeric.\n"))

  # AEDB.CEA <-  AEDB.CEA %>% mutate(AEDB.CEA[,var.temp] == replace(AEDB.CEA[,var.temp], AEDB.CEA[,var.temp]==0, NA))

  AEDB.CEA[,var.temp][AEDB.CEA[,var.temp]==0.000000]=NA

  cat(paste0("* standardising ", var.temp, 
             " (mean: ",round(mean(!is.na(AEDB.CEA[,var.temp])), digits = 6),
             ", n = ",sum(!is.na(AEDB.CEA[,var.temp])),").\n"))
  
  AEDB.CEA <- AEDB.CEA %>%
      mutate_at(vars(var.temp), 
        # list(Z = ~ (AEDB.CEA[,var.temp] - mean(AEDB.CEA[,var.temp], na.rm = TRUE))/sd(AEDB.CEA[,var.temp], na.rm = TRUE))
        list(RANK = ~ qnorm((rank(AEDB.CEA[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA[,var.temp]))))
      )
  # str(UCORBIOGSAqc$Z)
  cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
  AEDB.CEA[,var.temp.rank] <- NULL
  names(AEDB.CEA)[names(AEDB.CEA) == "RANK"] <- var.temp.rank
}

# rm(var.temp, var.temp.rank)

```

### Visualize transformations

We will just visualize these transformations.

```{r MCP1 vs Cytokines Histograms}
proteins_of_interest_rank_mcp1 <- c("MCP1_pg_ug_2015_rank", proteins_of_interest_rank)

proteins_of_interest_mcp1 <- c("MCP1_pg_ug_2015", proteins_of_interest)

for(PROTEIN in proteins_of_interest_mcp1){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "",
                    ggtheme = theme_minimal())
  print(p1)
  
}


for(PROTEIN in proteins_of_interest_rank_mcp1){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "inverse-normal transformation",
                    ggtheme = theme_minimal())
  print(p1)
  
}
  
```

### Correlations

Here we calculate correlations between `MCP1_pg_ug_2015` and 28 other cytokines (including `MCP1` as measured in experiment 1. We use Spearman's test, thus, correlations a given in _rho_. Please note the indications of measurement methods:

- _L_: LUMINEX
- _E_: ELISA
- _a_: activity assay

```{r MCP1 vs Cytokines correlations}
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)

# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c(proteins_of_interest_rank_mcp1)
                                    )

# str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

rename_proteins_of_interest_mcp1 <- c("MCP1 (L, exp2)", 
                                    "IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", 
                                    "IL13 (L)", "IL21 (L)", 
                                    "INFG", "TNFA", "MIF (L)", 
                                    "MCP1 (L, exp1)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)", 
                                    "Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)", 
                                    "OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
    )
}

corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)


names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"

DT::datatable(corr_biomarkers.rank.df)

fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))

```

```{r MCP1 vs Cytokines heatmap}
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           tl.cex = 6,
           # xlab = c("MCP1"),
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())
rm(p1)

```

While visually actractive we are not necessarily interested in the correlations between all the cytokines, rather of MCP1 with other cytokines only.

```{r MCP1 vs Cytokines barplot}
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp2)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold
p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
          fill = "CytokineY",               # change fill color by cyl
          # color = "white",            # Set bar border colors to white
          palette = uithof_color,            # jco journal color palett. see ?ggpar
          xlab = "Cytokine",
          ylab = expression("Spearman's"~italic(rho)),
          sort.val = "desc",          # Sort the value in dscending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 45, # Rotate vertically x axis texts
          cex = 0.8
          )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 9),
        axis.text.y = element_text(size = 9)) 

rm(p1)
```

Another version - problably not good. 
```{r MCP1 vs Cytokines dotchart}

p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
           color = "CytokineY",                                # Color by groups
           palette = uithof_color, # Custom color palette
           xlab = "Cytokine",
           ylab = expression(log[10]~"("~italic(p)~")-value"),
           ylim = c(0, 6),
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           rotate = FALSE,                                # Rotate vertically
           # group = "CytokineY",                                # Order by groups
           dot.size = 8,                                 # Large dot size
           label = round(temp$SpearmanRho, digits = 3),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 8, 
                             vjust = 0.5)                   
           )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 9),
        axis.text.y = element_text(size = 9))

# rm(temp, p1)

```


## MCP1 vs. cytokines plaque levels `lm()`

### Model 1

In this model we correct for _Age_ and _Gender_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: Cytokines - linear regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
```

```{r CrossSec: Cytokines - linear regression MODEL1 RANK Writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```



### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines as a function of serum/plaque MCP1 levels.
```{r CrossSec: Cytokines - linear regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
```

```{r CrossSec: Cytokines - linear regression MODEL2 RANK, writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```



# Session information

------

    Version:      v1.0.9
    Last update:  2020-06-26
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to analyse MCP1 from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
    
    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_
    * analysis on serum ASAP
    * linear regression models (model 1 and model 2) of `MCP1_pg_ug_2015` with cytokines DONE
    * check out the difference between the measuremens of `MCP1` and `MCP1_pg_ug_2015` MONDAY
    * double check the plotting of the MACE DONE
    * add the statistics for the correlation of `MCP1_pg_ug_2015` with the cytokines DONE
    
    _S_
    * prettify forest plot
    
    _C_
    
    
    _W_
    
    
    **Changes log**
    * v1.0.9 Added linear regression models for MCP1 vs. cytokines plaque levels. Double checked upload of MACE-plots. Added statistics from correlation (heatmap) to txt-file.
    * v1.0.8 Fixed error in MCP1 serum analysis. It turns out the `MCP1` and `MCP1_pg_ug_2015` variables are _both_ measured in plaque, in two separate experiments, exp. no. 1 and exp. no. 2, respectively. 
    * v1.0.7 Fixed the per Age-group MCP1 Box plots. Added correlations with other cytokines in plaques.
    * v1.0.6 Only analyses and figures that end up in the final manuscript.
    * v1.0.5 Update with 30- and 90-days survival.
    * v1.0.4 Updated with Cox-regressions.
    * v1.0.3 Included more models.
    * v1.0.2 Bugs fixed.
    * v1.0.1 Extended with linear and logistic regressions
    * v1.0.0 Inital version
    

------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))
```

------
<sup>&copy; 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup>
------

